From nobody Wed Mar 22 10:29:21 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B8807129AD1 for ; Wed, 22 Mar 2017 10:29:19 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.598 X-Spam-Level: X-Spam-Status: No, score=-2.598 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DC_PNG_UNO_LARGO=0.001, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id r0nE7up3S1Tc for ; Wed, 22 Mar 2017 10:29:16 -0700 (PDT) Received: from mail-qk0-x22a.google.com (mail-qk0-x22a.google.com [IPv6:2607:f8b0:400d:c09::22a]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5D5691294DF for ; Wed, 22 Mar 2017 10:29:13 -0700 (PDT) Received: by mail-qk0-x22a.google.com with SMTP id y76so161981379qkb.0 for ; Wed, 22 Mar 2017 10:29:13 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:from:date:message-id:subject:to; bh=fd2vN1LcmtSBHXWYhG0zVnWcPiCo7vKqJsvBwDbOBbE=; b=YVghGqUriGbnrgixxsP4C/KIZEp9x/6D8KN0hkDgxBc/JnfB8G2dNdO76QaE1Rd8Jd 0jlIItLl4qvz0ShBW3VCKJ+rcArWpIOs2Pji0LV0aIUSg4ULyJ2YomsaZyfUasXdqRmA pU4Ixz/kCEa88+KrjoP1/92ty294ORktgFMj6Umr91t4IkI/FLaek5U8LaePY3WXSNuG 1VL76vEfBGioor4dY5IZYOJApEM8foSF67uCz4m/YvQcA8KSFzR6P2xF/pRtoL3gVKtp 6gohVSYjDLVyetFyV3bdnBmRDkOzztGJncVtchpaLvOQDRcmssX1xyurQfb8fB20eYyU PO4Q== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:from:date:message-id:subject:to; bh=fd2vN1LcmtSBHXWYhG0zVnWcPiCo7vKqJsvBwDbOBbE=; b=R72yxF0Cz/qMxE9IT9/TceKZ2hS7UyF3XT07LQ6IlYNMnYi0hpqQbxLASHPcjkSSEE 3sErpj4/YaFGtZn6ahxnK6CPkxN9LLAm0TLczHAmePDGihxwGk4u2pHBUITlmJkj5TfI RjPxzeCE4ogzHQn5LTsbx3PIMq2EVtPx/AAV8A6t3H9ZBrWswxX9LhulUVlx3y5zv5cB zTH41pzV6HvXjOyGy2WyulGzT8GNona+NSA70eI1wOYPtiRQvxvB6nHiiU3i7jc2z79Y R1qw2F3WwQbSxgidgrIIra/fOBWgd1A3lwYlVvvDkkpvO1Va/RLwyf/qpj7cYZgmOq/A x+fw== X-Gm-Message-State: AFeK/H2CIXA3WmX1EyNIwLJyjMYSV1ByA9UzvTW1F/MWN32mMotylDFbbxnqj5uhlOMNsODG2UhB282vk7jXhg== X-Received: by 10.55.65.81 with SMTP id o78mr17444453qka.82.1490203752186; Wed, 22 Mar 2017 10:29:12 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Wed, 22 Mar 2017 10:29:11 -0700 (PDT) X-Originating-IP: [98.207.11.149] From: David Meyer Date: Wed, 22 Mar 2017 10:29:11 -0700 Message-ID: To: idnet@ietf.org Content-Type: multipart/mixed; boundary=001a114ac54e4b8be0054b551931 Archived-At: Subject: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 22 Mar 2017 17:29:20 -0000 --001a114ac54e4b8be0054b551931 Content-Type: multipart/alternative; boundary=001a114ac54e4b8bdc054b55192f --001a114ac54e4b8bdc054b55192f Content-Type: text/plain; charset=UTF-8 Folks, I thought I'd try to get some discussion going by outlining some of my views as to why networking is lagging other areas in the development and application of Machine Learning (ML). In particular, networking is way behind what we might call the "perceptual tasks" (vision, NLP, robotics, etc) as well as other areas (medicine, finance, ...). The attached slide from one of my decks tries to summarize the situation, but I'll give a bit of an outline below. So why is networking lagging many other fields when it comes to the application of machine learning? There are several reasons which I'll try to outline here (I was fortunate enough to discuss this with the packetpushers crew a few weeks ago, see [0]). These are in no particular order. First, we don't have a "useful" theory of networking (UTON). One way to think about what such a theory would look like is by analogy to what we see with the success of convolutional neural networks (CNNs) not only for vision but now for many other tasks. In that case there is a theory of how vision works, built up from concepts like receptive fields, shared weights, simple and complex cells, etc. For example, the input layer of a CNN isn't fully connected; rather connections reflect the receptive field of the input layer, which is in a way that is "inspired" by biological vision (being very careful with "biological inspiration"). Same with the alternation of convolutional and pooling layers; these loosely model the alternation of simple and complex cells in the primary visual cortex (V1), the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a theory seems to be required for transfer learning [1], which we'll need if we don't want every network to be analyzed in an ad-hoc, one-off style (like we see today). The second thing that we need to think about is publicly available standardized data sets. Examples here include MNIST, ImageNet, and many others. The result of having these data sets has been the steady ratcheting down of error rates on tasks such as object and scene recognition, NLP, and others to super-human levels. Suffice it to say we have nothing like these data sets for networking. Networking data sets today are largely proprietary, and because there is no UTON, there is no real way to compare results between them. Third, there is a large skill set gap. Network engineers (us!) typically don't have the mathematical background required to build effective machine learning at scale. See [2] for an outline of some of the mathematical skills that are essential for effective ML. There is a lot more to this, involving how progress is made in ML (open data, open source, open models, in general open science and associated communities, see e.g., OpenAi [3], Distill [4], and many others). In any event we need build community and gain new skills if we want to be able to develop and apply state of the art machine learning algorithms to network data, at scale. The bottom line is that it will be difficult if not impossible to be effective in the ML space if we ourselves don't understand how it works and further, if we can build explainable systems (noting that explaining what the individual neurons in a deep neural network are doing is notoriously difficult; that said much progress is being made). So we want to build explainable, end-to-end trained systems, and to accomplish this we ourselves need to understand how these algorithms work, but in training and in inference. This email is already TL;DR but I'll add one more here: We need to learn control, not just prediction. Since we live in an inherently adversarial environment we need to take advantage of Reinforcement Learning as well as the various attacks being formulated against ML; [5] gives one interesting example of attacks against policy networks using adversarial examples. See also slides 31 and 32 of [6] for some more on this topic. I hope some of this gets us thinking about the problems we need to solve in order to be successful in the ML space. There's plenty more of this on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. I'm looking forward to the discussion. Thanks, --dmm [0] http://packetpushers.net/podcast/podcasts/pq-show-107-applicability-machine-learning-networking/ [1] http://sebastianruder.com/transfer-learning/index.html [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ [3] https://openai.com/blog/ [4] http://distill.pub/ [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx --001a114ac54e4b8bdc054b55192f Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Folks,

I thought I'd try= to get some discussion going by outlining some of my views as to why netwo= rking is lagging other areas in the development and application of Machine = Learning (ML). In particular, networking is way behind what we might call t= he "perceptual tasks" (vision, NLP, robotics, etc) as well as oth= er areas (medicine, finance, ...). The attached slide from one of my decks = tries to summarize the situation, but I'll give a bit of an outline bel= ow.=C2=A0

So why is networking lagging many other fields when i= t comes to the application of machine learning? There are several reasons w= hich I'll try to outline here (I was fortunate enough to discuss this w= ith the packetpushers crew a few weeks ago, see [0]). These are in no parti= cular order.

First, we don't have a "useful" theo= ry of networking (UTON). One way to think about what such a theory would lo= ok like is by analogy to what we see with the success of convolutional neur= al networks (CNNs) not only for vision but now for many other tasks. In tha= t case there is a theory of how vision works, built up from concepts like r= eceptive fields, shared weights, simple and complex cells, etc. For example= , the input layer of a CNN isn't fully connected; rather connections re= flect the receptive field of the input layer, which is in a way that is &qu= ot;inspired" by biological vision (being very careful with "biolo= gical inspiration"). Same with the alternation of convolutional and po= oling layers; these loosely model the alternation of simple and complex cel= ls in the primary visual cortex (V1), the secondary visual cortex(V2) and t= he Brodmann area (V3).=C2=A0BTW, such a theory seems to be required for tra= nsfer learning [1], which we'll need if we don't want every network= to be analyzed in an ad-hoc, one-off style (like we see today).

The second thing that we need to think about is publicly available standa= rdized data sets. Examples here include MNIST, ImageNet, and many others. T= he result of having these data sets has been the steady=C2=A0ratcheting dow= n of error rates on tasks such as object and scene recognition, NLP, and ot= hers to super-human levels. Suffice it to say we have nothing like these da= ta sets for networking. Networking data sets today are largely proprietary,= and because there is no UTON, there is no real way to compare results betw= een them.

Third, there is a large skill set gap. Network engine= ers (us!) typically don't have the mathematical background required to = build effective machine learning at scale. See [2] for an outline of some o= f the mathematical skills that are essential for effective ML. There is a l= ot more to this, involving how progress is made in ML (open data, open sour= ce, open models, in general open science and associated communities, see e.= g., OpenAi [3], Distill [4], and many others). In any event we need build c= ommunity and gain new skills if we want to be able to develop and apply sta= te of the art machine learning algorithms to network data, at scale. The bo= ttom line is that it will be difficult if not impossible to be effective in= the ML space if we ourselves don't understand how it works and further= , if we can build explainable systems (noting that explaining what the indi= vidual neurons in a deep neural network are doing is notoriously difficult;= that said much progress is being made). So we want to build explainable, e= nd-to-end trained systems, and to accomplish this we ourselves need to unde= rstand how these algorithms work, but in training and in inference.<= /div>

This email is already TL;DR but I'll add one more here: We need to= learn control, not just prediction. Since we live in an inherently adversa= rial environment we need to take advantage of Reinforcement Learning as wel= l as the various attacks being formulated against ML; [5] gives one interes= ting example of attacks against policy networks using adversarial examples.= See also slides 31 and 32 of [6] for some more on this topic.
=

= I hope some of this gets us thinking about the problems we need to solve in= order to be successful in the ML space. There's plenty more of this on= http://www.1-4-5.net/~dmm/ml = and http://www.1-4-5.net/~d= mm/vita.html.
I'm looking = forward to the discussion.

Thanks,

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From: Rana Pratap Sircar To: David Meyer , "idnet@ietf.org" Thread-Topic: [Idnet] A few ideas/suggestions to get us going Thread-Index: AQHSozHu5+2wwRwqjU2j6iMywUBG/aGh7zZQ Date: Thu, 23 Mar 2017 08:42:36 +0000 Message-ID: References: In-Reply-To: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: authentication-results: 1-4-5.net; dkim=none (message not signed) header.d=none;1-4-5.net; dmarc=none action=none header.from=ericsson.com; x-originating-ip: [122.15.11.155] x-microsoft-exchange-diagnostics: 1; VI1PR0701MB1934; 7:qh/85lVCyWfHl6rhf+b6rqqYhnC0/0CbcsGOFLcpy6m5/AJQaBWoywfOkQu79QXwvR51m9BErzLsbfIMOrBy/SEMeOaulYqHJJMUz1icfQsxKbWdzFRK7PlYtyaZ/JsdRvgrcxvVgHVOjbkXF4RjmhFZeojUuAZuluC67bkctcxgiDa9V3sIRJIhNqNvZF2VKpqfVoI4x6QqDS06Pph5sj2OVQ5axU7Vs6XJse1KrthL+/qE78D7kwEXq3zqUUNmXvf0G8Vc5Oc+xs00VT0fLtG6/AFn7AhKf52cgTWh82iAaUtG4Z65xV0XbGwMYrxUqEmhM03WAJSdLtfCVpw8gA== x-ms-office365-filtering-correlation-id: 125c3f71-23a0-40bb-cba4-08d471c88e94 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URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 8ZFbToPplUX3 for ; Thu, 23 Mar 2017 02:56:06 -0700 (PDT) Received: from mailext.sit.fraunhofer.de (mailext.sit.fraunhofer.de [141.12.72.89]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 1BBBC13150E for ; Thu, 23 Mar 2017 02:56:05 -0700 (PDT) Received: from mail.sit.fraunhofer.de (mail.sit.fraunhofer.de [141.12.84.171]) by mailext.sit.fraunhofer.de (8.14.4/8.14.4/Debian-2ubuntu2.1) with ESMTP id v2N9tw34011476 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES256-GCM-SHA384 bits=256 verify=NOT) for ; Thu, 23 Mar 2017 10:56:01 +0100 Received: from [134.102.161.4] (134.102.161.4) by mail.sit.fraunhofer.de (141.12.84.171) with Microsoft SMTP Server (TLS) id 14.3.319.2; Thu, 23 Mar 2017 10:55:53 +0100 To: References: From: Henk Birkholz Message-ID: Date: Thu, 23 Mar 2017 10:55:48 +0100 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.7.0 MIME-Version: 1.0 In-Reply-To: Content-Type: multipart/signed; protocol="application/pkcs7-signature"; micalg=sha-256; boundary="------------ms080100010201010305000302" X-Originating-IP: [134.102.161.4] Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 23 Mar 2017 09:56:10 -0000 --------------ms080100010201010305000302 Content-Type: text/plain; charset=windows-1252; format=flowed Content-Transfer-Encoding: quoted-printable Hello, maybe an excerpt from my personal point of view can be a contribution to = the discussion starting on this list. In my experience, the gap between... work-flows (in lack of a better=20 term) how problem statements are created in the domain of=20 network/management/security and - in contrast - the domain of machine=20 learning is (aka "appears to me, subjectively") astonishingly vast. One way to illustrate that gap (and there are multiple ways, I think),=20 using a bit of hyperbole: "network: least viable solution" -> "I only produce the information I require" meets "machine learning: combination of heterogeneous most viable solutions"=20 -> "Please provide me with everything you got, including the best=20 qualified semantic annotation of characteristics and context so I can=20 identify and select the features that are relevant to provide a=20 contribution". Also - as trivial and repetitive as that might sound - terminology again = is key. Please note the following quote I actually encountered in my=20 very early days collaborating with machine learning architects: "The=20 maximum number of ports really is 2^16? Wow, how big can these routers be= ?". While this is of course "one of these entertaining anecdotes" everybody=20 already heard at least once already, it also highlights quite=20 prominently the existing gap form a different angle. In consequence, guidance that enables an individual with a=20 specialization in machine learning skills to just better understand the=20 network domain itself - maybe by illustrating very simple, well-known=20 and already solved problem statements - might already be a contribution=20 of high value, just because it is specifically provided for that group=20 of individuals and using terminology that is common and well-understood=20 in that domain. Viele Gr=FC0e, Henk On 03/22/2017 06:29 PM, David Meyer wrote: > Folks, > > I thought I'd try to get some discussion going by outlining some of my > views as to why networking is lagging other areas in the development an= d > application of Machine Learning (ML). In particular, networking is way > behind what we might call the "perceptual tasks" (vision, NLP, robotics= , > etc) as well as other areas (medicine, finance, ...). The attached slid= e > from one of my decks tries to summarize the situation, but I'll give a > bit of an outline below. > > So why is networking lagging many other fields when it comes to the > application of machine learning? There are several reasons which I'll > try to outline here (I was fortunate enough to discuss this with the > packetpushers crew a few weeks ago, see [0]). These are in no particula= r > order. > > First, we don't have a "useful" theory of networking (UTON). One way to= > think about what such a theory would look like is by analogy to what we= > see with the success of convolutional neural networks (CNNs) not only > for vision but now for many other tasks. In that case there is a theory= > of how vision works, built up from concepts like receptive fields, > shared weights, simple and complex cells, etc. For example, the input > layer of a CNN isn't fully connected; rather connections reflect the > receptive field of the input layer, which is in a way that is "inspired= " > by biological vision (being very careful with "biological inspiration")= =2E > Same with the alternation of convolutional and pooling layers; these > loosely model the alternation of simple and complex cells in the primar= y > visual cortex (V1), the secondary visual cortex(V2) and the Brodmann > area (V3). BTW, such a theory seems to be required for transfer learnin= g > [1], which we'll need if we don't want every network to be analyzed in > an ad-hoc, one-off style (like we see today). > > The second thing that we need to think about is publicly available > standardized data sets. Examples here include MNIST, ImageNet, and many= > others. The result of having these data sets has been the > steady ratcheting down of error rates on tasks such as object and scene= > recognition, NLP, and others to super-human levels. Suffice it to say w= e > have nothing like these data sets for networking. Networking data sets > today are largely proprietary, and because there is no UTON, there is n= o > real way to compare results between them. > > Third, there is a large skill set gap. Network engineers (us!) typicall= y > don't have the mathematical background required to build effective > machine learning at scale. See [2] for an outline of some of the > mathematical skills that are essential for effective ML. There is a lot= > more to this, involving how progress is made in ML (open data, open > source, open models, in general open science and associated communities= , > see e.g., OpenAi [3], Distill [4], and many others). In any event we > need build community and gain new skills if we want to be able to > develop and apply state of the art machine learning algorithms to > network data, at scale. The bottom line is that it will be difficult if= > not impossible to be effective in the ML space if we ourselves don't > understand how it works and further, if we can build explainable system= s > (noting that explaining what the individual neurons in a deep neural > network are doing is notoriously difficult; that said much progress is > being made). So we want to build explainable, end-to-end trained > systems, and to accomplish this we ourselves need to understand how > these algorithms work, but in training and in inference. > > This email is already TL;DR but I'll add one more here: We need to lear= n > control, not just prediction. Since we live in an inherently adversaria= l > environment we need to take advantage of Reinforcement Learning as well= > as the various attacks being formulated against ML; [5] gives one > interesting example of attacks against policy networks using adversaria= l > examples. See also slides 31 and 32 of [6] for some more on this topic.= > > I hope some of this gets us thinking about the problems we need to solv= e > in order to be successful in the ML space. There's plenty more of this > on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html= =2E > I'm looking forward to the discussion. > > Thanks, > > --dmm > > > > > [0] http://packetpushers.net/podcast/podcasts/pq-show-107-applicabilit= y-machine-learning-networking/ > > [1] http://sebastianruder.com/transfer-learning/index.html > [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learnin= g/ > [3] https://openai.com/blog/ > [4] http://distill.pub/ > [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pd= f > [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --------------ms080100010201010305000302 Content-Type: application/pkcs7-signature; name="smime.p7s" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="smime.p7s" Content-Description: S/MIME Cryptographic 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X-Gm-Message-State: AFeK/H0El9oTFBge5A7pwa5dZ0D7fAtfivHMvEfCGjpfotgYIr3nWvJe6Ocoz3NN61gDagvNcoSWBKwbdTYdjA== X-Received: by 10.200.47.9 with SMTP id j9mr2450335qta.57.1490276132081; Thu, 23 Mar 2017 06:35:32 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Thu, 23 Mar 2017 06:35:31 -0700 (PDT) X-Originating-IP: [64.134.235.66] In-Reply-To: References: From: David Meyer Date: Thu, 23 Mar 2017 06:35:31 -0700 Message-ID: To: Rana Pratap Sircar Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a113773b4789736054b65f3a0 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 23 Mar 2017 13:35:38 -0000 --001a113773b4789736054b65f3a0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Rana, On Thu, Mar 23, 2017 at 1:42 AM, Rana Pratap Sircar < rana.pratap.sircar@ericsson.com> wrote: > Hi Dave, > > > > As always another wonderful initiative from you. I too have been > struggling with the 3 points that you mentioned =E2=80=93 > > 1. Theory of Networking > > 2. Open Datasets that are relevant to Networks and not the images > and demography aspects =E2=80=93 not too sure of the initiatives such as > https://github.com/opentraffic & http://www.caida.org/data/ > > 3. Skills > > > > I feel that apart from this, there are a couple of additional challenges = =E2=80=93 > > 1. Networks have a fairly complex layered architecture. Thus, any > problem statement needs to look at a smaller scope (assuming that behavio= r > of network is sum of these scopes ~ which, in my humble opinion, is > incorrect) > dmm> definitely; this goes with the "usable" theory of networking > 2. Continuously and rapidly evolving technology > dmm> Yes. One way to think about this is that in many cases ML models assume a "stationary" underlying Data Generation Distribution [0]; obviously this isn't isn't the case in adversarial situations (this is why "baselines" are weak in anomaly detection scenarios; an attacker merely observes the black-box behavior and changes behavior accordingly) or in any other case in which the underlying processes change (for example, in APT scenarios). I will point out here that this is one place where distributed representations can help; see [1] for a really nice overview of representation theory. Thanks, Dave [0] The underlying data generating distribution (DGD) is the process or set of processes that generate the data we observe; in some sense the observations are a proxy for this DGD. The behavior of these processes is what we really want to understand. [1] https://arxiv.org/pdf/1305.0445.pdf > > I would most definitely like to participate & contribute=E2=80=A6 > > > > Best regards, > > Rana > > Ph: +91 88 00 22 4872 <+91%2088002%2024872> > > "You can't make the same mistake twice, the second time, it's not a > mistake, it's a choice." - Anonymous > > > > *From:* IDNET [mailto:idnet-bounces@ietf.org] *On Behalf Of *David Meyer > *Sent:* Wednesday, March 22, 2017 10:59 PM > *To:* idnet@ietf.org > *Subject:* [Idnet] A few ideas/suggestions to get us going > > > > Folks, > > > > I thought I'd try to get some discussion going by outlining some of my > views as to why networking is lagging other areas in the development and > application of Machine Learning (ML). In particular, networking is way > behind what we might call the "perceptual tasks" (vision, NLP, robotics, > etc) as well as other areas (medicine, finance, ...). The attached slide > from one of my decks tries to summarize the situation, but I'll give a bi= t > of an outline below. > > > > So why is networking lagging many other fields when it comes to the > application of machine learning? There are several reasons which I'll try > to outline here (I was fortunate enough to discuss this with the > packetpushers crew a few weeks ago, see [0]). These are in no particular > order. > > > > First, we don't have a "useful" theory of networking (UTON). One way to > think about what such a theory would look like is by analogy to what we s= ee > with the success of convolutional neural networks (CNNs) not only for > vision but now for many other tasks. In that case there is a theory of ho= w > vision works, built up from concepts like receptive fields, shared weight= s, > simple and complex cells, etc. For example, the input layer of a CNN isn'= t > fully connected; rather connections reflect the receptive field of the > input layer, which is in a way that is "inspired" by biological vision > (being very careful with "biological inspiration"). Same with the > alternation of convolutional and pooling layers; these loosely model the > alternation of simple and complex cells in the primary visual cortex (V1)= , > the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a > theory seems to be required for transfer learning [1], which we'll need i= f > we don't want every network to be analyzed in an ad-hoc, one-off style > (like we see today). > > > > The second thing that we need to think about is publicly available > standardized data sets. Examples here include MNIST, ImageNet, and many > others. The result of having these data sets has been the steady ratcheti= ng > down of error rates on tasks such as object and scene recognition, NLP, a= nd > others to super-human levels. Suffice it to say we have nothing like thes= e > data sets for networking. Networking data sets today are largely > proprietary, and because there is no UTON, there is no real way to compar= e > results between them. > > > > Third, there is a large skill set gap. Network engineers (us!) typically > don't have the mathematical background required to build effective machin= e > learning at scale. See [2] for an outline of some of the mathematical > skills that are essential for effective ML. There is a lot more to this, > involving how progress is made in ML (open data, open source, open models= , > in general open science and associated communities, see e.g., OpenAi [3], > Distill [4], and many others). In any event we need build community and > gain new skills if we want to be able to develop and apply state of the a= rt > machine learning algorithms to network data, at scale. The bottom line is > that it will be difficult if not impossible to be effective in the ML spa= ce > if we ourselves don't understand how it works and further, if we can buil= d > explainable systems (noting that explaining what the individual neurons i= n > a deep neural network are doing is notoriously difficult; that said much > progress is being made). So we want to build explainable, end-to-end > trained systems, and to accomplish this we ourselves need to understand h= ow > these algorithms work, but in training and in inference. > > > > This email is already TL;DR but I'll add one more here: We need to learn > control, not just prediction. Since we live in an inherently adversarial > environment we need to take advantage of Reinforcement Learning as well a= s > the various attacks being formulated against ML; [5] gives one interestin= g > example of attacks against policy networks using adversarial examples. Se= e > also slides 31 and 32 of [6] for some more on this topic. > > > > I hope some of this gets us thinking about the problems we need to solve > in order to be successful in the ML space. There's plenty more of this on > http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. > > I'm looking forward to the discussion. > > > > Thanks, > > > > --dmm > > > > > > > > > > [0] http://packetpushers.net/podcast/podcasts/pq-show-107- > applicability-machine-learning-networking/ > > [1] http://sebastianruder.com/transfer-learning/index.html > > [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ > > [3] https://openai.com/blog/ > > [4] http://distill.pub/ > > [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf > > [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx > --001a113773b4789736054b65f3a0 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hey Rana,


On Thu, Mar 23, 2017 at 1:42 AM, Rana Pratap Sircar <rana.pratap.sircar@ericsson.com> wrote:

Hi Dave,

=C2=A0

As always another wonderful initiative from you. I too have been = struggling with the 3 points that you mentioned =E2=80=93

1.= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Theory of Networking

2.= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Open Datasets that are relevant to Networks and not the imag= es and demography aspects =E2=80=93 not too sure of the initiatives such as https://github= .com/opentraffic & http://www.caida.org/data/

3.= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Skills

=C2=A0

I feel that apart from this, there are a couple of additional cha= llenges =E2=80=93

1.= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Networks have a fairly complex layered architecture. Thus, a= ny problem statement needs to look at a smaller scope (assuming that behavi= or of network is sum of these scopes ~ which, in my humble opinion, is incorrect)

=

dmm> definitely; this goes with the &qu= ot;usable" theory of networking =C2=A0

=

2.= =C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Continuously and rapidly evolving technology

dmm> Yes. One way to think about this is that i= n many cases ML models assume a "stationary" underlying Data Gene= ration Distribution [0]; obviously this isn't isn't the case in adv= ersarial situations (this is why "baselines" are weak in anomaly = detection scenarios; an attacker merely observes the black-box behavior and= changes behavior accordingly) or in any other case in which the underlying= processes change (for example, in APT scenarios). I will point out here th= at this is one place where distributed representations can help; see [1] fo= r a really nice overview of representation theory.

Thanks,

Dave


<= div>[0] The underlying data generating distribution (DGD) =C2=A0is the proc= ess or set of processes that generate the data we observe; in some sense th= e observations are a proxy for this DGD. The behavior of these processes is= what we really want to understand.

[1]=C2=A0<= a href=3D"https://arxiv.org/pdf/1305.0445.pdf">https://arxiv.org/pdf/1305.0= 445.pdf

=C2=A0

I would most definitely like to participate & contribute=E2= =80=A6

=C2=A0

Best regards,

Rana

Ph: +91 88 00 22 4872

"You can't make the same mistake twice, the second time,= it's not a mistake, it's a choice." - Anonymous=

=C2=A0

From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of David Meyer
Sent: Wednesday, March 22, 2017 10:59 PM
To: idnet@ietf.o= rg
Subject: [Idnet] A few ideas/suggestions to get us going

=C2=A0

Folks,<= /u>

=C2=A0

I thought I'd try to= get some discussion going by outlining some of my views as to why networki= ng is lagging other areas in the development and application of Machine Lea= rning (ML). In particular, networking is way behind what we might call the "perceptual tasks" (vision, NL= P, robotics, etc) as well as other areas (medicine, finance, ...). The atta= ched slide from one of my decks tries to summarize the situation, but I'= ;ll give a bit of an outline below.=C2=A0

=C2=A0

So why is networking lag= ging many other fields when it comes to the application of machine learning= ? There are several reasons which I'll try to outline here (I was fortu= nate enough to discuss this with the packetpushers crew a few weeks ago, see [0]). These are in no particular order.

=C2=A0

First, we don't have= a "useful" theory of networking (UTON). One way to think about w= hat such a theory would look like is by analogy to what we see with the suc= cess of convolutional neural networks (CNNs) not only for vision but now for many other tasks. In that case there is a theory of= how vision works, built up from concepts like receptive fields, shared wei= ghts, simple and complex cells, etc. For example, the input layer of a CNN = isn't fully connected; rather connections reflect the receptive field of the input layer, which is in a way that is = "inspired" by biological vision (being very careful with "bi= ological inspiration"). Same with the alternation of convolutional and= pooling layers; these loosely model the alternation of simple and complex cells in the primary visual cortex (V1), the seconda= ry visual cortex(V2) and the Brodmann area (V3).=C2=A0BTW, such a theory se= ems to be required for transfer learning [1], which we'll need if we do= n't want every network to be analyzed in an ad-hoc, one-off style (like we see today).

=C2=A0

The second thing that we= need to think about is publicly available standardized data sets. Examples= here include MNIST, ImageNet, and many others. The result of having these = data sets has been the steady=C2=A0ratcheting down of error rates on tasks such as object and scene recognition, NLP, an= d others to super-human levels. Suffice it to say we have nothing like thes= e data sets for networking. Networking data sets today are largely propriet= ary, and because there is no UTON, there is no real way to compare results between them.=

=C2=A0

Third, there is a large = skill set gap. Network engineers (us!) typically don't have the mathema= tical background required to build effective machine learning at scale. See= [2] for an outline of some of the mathematical skills that are essential for effective ML. There is a lot more to this, i= nvolving how progress is made in ML (open data, open source, open models, i= n general open science and associated communities, see e.g., OpenAi [3], Di= still [4], and many others). In any event we need build community and gain new skills if we want to be abl= e to develop and apply state of the art machine learning algorithms to netw= ork data, at scale. The bottom line is that it will be difficult if not imp= ossible to be effective in the ML space if we ourselves don't understand how it works and further, if we= can build explainable systems (noting that explaining what the individual = neurons in a deep neural network are doing is notoriously difficult; that s= aid much progress is being made). So we want to build explainable, end-to-end trained systems, and to accomplis= h this we ourselves need to understand how these algorithms work, but in tr= aining and in inference.

=C2=A0

This email is already TL= ;DR but I'll add one more here: We need to learn control, not just pred= iction. Since we live in an inherently adversarial environment we need to t= ake advantage of Reinforcement Learning as well as the various attacks being formulated against ML; [5] gives one = interesting example of attacks against policy networks using adversarial ex= amples. See also slides 31 and 32 of [6] for some more on this topic.

=C2=A0

I hope some of this gets= us thinking about the problems we need to solve in order to be successful = in the ML space. There's plenty more of this on http://www.1-4-5= .net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html.

I'm looking forward = to the discussion.

=C2=A0

Thanks,=

=C2=A0

--dmm

=C2=A0

=C2=A0

=C2=A0

=C2=A0


--001a113773b4789736054b65f3a0-- From nobody Thu Mar 23 06:51:45 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 4D278129722 for ; Thu, 23 Mar 2017 06:51:43 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.898 X-Spam-Level: X-Spam-Status: No, score=-1.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 1ERoKjluE9Ys for ; Thu, 23 Mar 2017 06:51:39 -0700 (PDT) Received: from mail-qt0-x232.google.com (mail-qt0-x232.google.com [IPv6:2607:f8b0:400d:c0d::232]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D94E9126C26 for ; Thu, 23 Mar 2017 06:51:38 -0700 (PDT) Received: by mail-qt0-x232.google.com with SMTP id r45so175487080qte.3 for ; Thu, 23 Mar 2017 06:51:38 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=ikaOcIG/e+ZNpRzstzgx+yJuZgLIHrQTwvAMPfYY6BY=; b=irf3dBUcdke/S8DtIlXkh8v5KUO+wOCIBI1jf+WslqzERl/oqbz46k1oqYaRM2wPJ5 v7zTXG37cqMhsV4hfFrBMpgL8PyWzMCmZKmA9SgOCdO89a/36p8KYmj/lxPRHL4bDc+d Ucn0xEWuQeGikcpfhA7cBzeoG69SgxbjcIhZW2GuW9HzVGbGYPgFOu8h8OcQjdZJvst+ LbyojMSjDvc5MxvN7zUtTbVZ61JCIJoPMEOuZRBEMS1/8gz0QmhdOHOvuYs49qAVLa8D plvfMeWbBxkGlM++ubhPD2sIpeDzanQMalgj04aGfEUiX6F2P5GtpumSHcUsSwwWgx/9 iR+w== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=ikaOcIG/e+ZNpRzstzgx+yJuZgLIHrQTwvAMPfYY6BY=; b=rmklxPpm2oEjSSPO71W1Q8j2agw/MwRVi+hkemIjrlB3TWH1spBLdUEmdRrzkD6HoR sKodBUwHP2l5GT/RLG9/USehxfZtR69q21uPJEBkt1fBSYFjxI3305fNGAHg6z+kHZKO q5Y8MzE51dws2eFhL2K3pxQxS31nyFO1KssaEKbGkqG6Q3UBzD3CxZTlaaumtjpe4DYI TW8uHlmVwBM6h5JZY/ldUWQBwn59oh4hAGQOpV4bjorspXwLihVGuNwiw0OVndxOX05P 19IIcfotsFcvUBFlOxH2FlGSly/DfCkdBs1RgecPdtc+mJr9zkSeA2Tf1PsazqtzEzZa sERw== X-Gm-Message-State: AFeK/H2u247+fLI/VdI6dNHT0aY+hdXDOereUvZNQ9InsxbI2oAh7WVVqCinRhiQqVH0Z+Oax7V70SzpM6LWkg== X-Received: by 10.237.50.133 with SMTP id z5mr2405754qtd.203.1490277097735; Thu, 23 Mar 2017 06:51:37 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Thu, 23 Mar 2017 06:51:36 -0700 (PDT) X-Originating-IP: [64.134.235.66] In-Reply-To: References: From: David Meyer Date: Thu, 23 Mar 2017 06:51:36 -0700 Message-ID: To: Henk Birkholz Cc: idnet@ietf.org Content-Type: multipart/mixed; boundary=94eb2c1252bc08013e054b662db0 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 23 Mar 2017 13:51:43 -0000 --94eb2c1252bc08013e054b662db0 Content-Type: multipart/alternative; boundary=94eb2c1252bc08013a054b662dae --94eb2c1252bc08013a054b662dae Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Henk, On Thu, Mar 23, 2017 at 2:55 AM, Henk Birkholz < henk.birkholz@sit.fraunhofer.de> wrote: > Hello, > > maybe an excerpt from my personal point of view can be a contribution to > the discussion starting on this list. > > > In my experience, the gap between... work-flows (in lack of a better term= ) > how problem statements are created in the domain of > network/management/security and - in contrast - the domain of machine > learning is (aka "appears to me, subjectively") astonishingly vast. > Definitely. See the few attached slides for a some ideas I have on the topic from upcoming talks. > > One way to illustrate that gap (and there are multiple ways, I think), > using a bit of hyperbole: > > "network: least viable solution" -> "I only produce the information I > require" > > meets > > "machine learning: combination of heterogeneous most viable solutions" -> > "Please provide me with everything you got, including the best qualified > semantic annotation of characteristics and context so I can identify and > select the features that are relevant to provide a contribution". > > > Also - as trivial and repetitive as that might sound - terminology again > is key. Please note the following quote I actually encountered in my very > early days collaborating with machine learning architects: "The maximum > number of ports really is 2^16? Wow, how big can these routers be?". > While this is of course "one of these entertaining anecdotes" everybody > already heard at least once already, it also highlights quite prominently > the existing gap form a different angle. > > > In consequence, guidance that enables an individual with a specialization > in machine learning skills to just better understand the network domain > itself - maybe by illustrating very simple, well-known and already solved > problem statements - might already be a contribution of high value, just > because it is specifically provided for that group of individuals and usi= ng > terminology that is common and well-understood in that domain. > I think what you are saying is that domain knowledge is very important (key) when building ML solutions. That is for sure true. Regarding terminology: it is a mess in ML: everyone uses their own notation. Consider the notation used in [0] vs. for example, the Deep Learning Book [1]. Compare the notation in [0] to say, Chapter 6 of [1]. So we don't yet agree even on mathematical notation (though the notation of [0] isn't widely used) much less the description of networks. It goes on. I'll just point out here that this again goes to not having a "usable" theory of network (UTON, I guess :-)). Thanks, Dave [0] https://arxiv.org/pdf/1404.7828.pdf [1] http://www.deeplearningbook.org/ > > Viele Gr=C3=BC0e, > > Henk > > > On 03/22/2017 06:29 PM, David Meyer wrote: > >> Folks, >> >> I thought I'd try to get some discussion going by outlining some of my >> views as to why networking is lagging other areas in the development and >> application of Machine Learning (ML). In particular, networking is way >> behind what we might call the "perceptual tasks" (vision, NLP, robotics, >> etc) as well as other areas (medicine, finance, ...). The attached slide >> from one of my decks tries to summarize the situation, but I'll give a >> bit of an outline below. >> >> So why is networking lagging many other fields when it comes to the >> application of machine learning? There are several reasons which I'll >> try to outline here (I was fortunate enough to discuss this with the >> packetpushers crew a few weeks ago, see [0]). These are in no particular >> order. >> >> First, we don't have a "useful" theory of networking (UTON). One way to >> think about what such a theory would look like is by analogy to what we >> see with the success of convolutional neural networks (CNNs) not only >> for vision but now for many other tasks. In that case there is a theory >> of how vision works, built up from concepts like receptive fields, >> shared weights, simple and complex cells, etc. For example, the input >> layer of a CNN isn't fully connected; rather connections reflect the >> receptive field of the input layer, which is in a way that is "inspired" >> by biological vision (being very careful with "biological inspiration"). >> Same with the alternation of convolutional and pooling layers; these >> loosely model the alternation of simple and complex cells in the primary >> visual cortex (V1), the secondary visual cortex(V2) and the Brodmann >> area (V3). BTW, such a theory seems to be required for transfer learning >> [1], which we'll need if we don't want every network to be analyzed in >> an ad-hoc, one-off style (like we see today). >> >> The second thing that we need to think about is publicly available >> standardized data sets. Examples here include MNIST, ImageNet, and many >> others. The result of having these data sets has been the >> steady ratcheting down of error rates on tasks such as object and scene >> recognition, NLP, and others to super-human levels. Suffice it to say we >> have nothing like these data sets for networking. Networking data sets >> today are largely proprietary, and because there is no UTON, there is no >> real way to compare results between them. >> >> Third, there is a large skill set gap. Network engineers (us!) typically >> don't have the mathematical background required to build effective >> machine learning at scale. See [2] for an outline of some of the >> mathematical skills that are essential for effective ML. There is a lot >> more to this, involving how progress is made in ML (open data, open >> source, open models, in general open science and associated communities, >> see e.g., OpenAi [3], Distill [4], and many others). In any event we >> need build community and gain new skills if we want to be able to >> develop and apply state of the art machine learning algorithms to >> network data, at scale. The bottom line is that it will be difficult if >> not impossible to be effective in the ML space if we ourselves don't >> understand how it works and further, if we can build explainable systems >> (noting that explaining what the individual neurons in a deep neural >> network are doing is notoriously difficult; that said much progress is >> being made). So we want to build explainable, end-to-end trained >> systems, and to accomplish this we ourselves need to understand how >> these algorithms work, but in training and in inference. >> >> This email is already TL;DR but I'll add one more here: We need to learn >> control, not just prediction. Since we live in an inherently adversarial >> environment we need to take advantage of Reinforcement Learning as well >> as the various attacks being formulated against ML; [5] gives one >> interesting example of attacks against policy networks using adversarial >> examples. See also slides 31 and 32 of [6] for some more on this topic. >> >> I hope some of this gets us thinking about the problems we need to solve >> in order to be successful in the ML space. There's plenty more of this >> on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. >> I'm looking forward to the discussion. >> >> Thanks, >> >> --dmm >> >> >> >> >> [0] http://packetpushers.net/podcast/podcasts/pq-show-107-applic >> ability-machine-learning-networking/ >> >> [1] http://sebastianruder.com/transfer-learning/index.html >> [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning= / >> [3] https://openai.com/blog/ >> [4] http://distill.pub/ >> [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf >> [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx >> >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --94eb2c1252bc08013a054b662dae Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
--
Some say he really tries to learn efficientl= y.
--001a113df38c1370b5054bce9adb-- From nobody Tue Mar 28 11:27:12 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 0D3D81299CD for ; Tue, 28 Mar 2017 11:27:01 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.9 X-Spam-Level: X-Spam-Status: No, score=-6.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, RP_MATCHES_RCVD=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id mDcfqdh6jWXj for ; Tue, 28 Mar 2017 11:26:57 -0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id F31C8129893 for ; Tue, 28 Mar 2017 11:26:54 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.36,237,1486422000"; d="scan'208,217";a="266610350" Received: from dhcp-8907.meeting.ietf.org (HELO [31.133.139.7]) ([31.133.139.7]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 28 Mar 2017 20:26:42 +0200 To: Brian Njenga , Oscar Mauricio Caicedo Rendon , Sheng Jiang References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> Cc: "idnet@ietf.org" From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: Date: Tue, 28 Mar 2017 20:26:36 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: Content-Type: multipart/alternative; boundary="------------FB54D18EFA9058C72F5D358D" Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:27:01 -0000 This is a multi-part message in MIME format. --------------FB54D18EFA9058C72F5D358D Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi, Le 28/03/2017 =C3=A0 20:06, Brian Njenga a =C3=A9crit : > > I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in t= he > design of the internet's architecture are continuing, albeit under a > different name. I'd like to participate remotely as well. > > I have a question that someone more thoughtful=E2=80=8B than I could an= swer: > Are there research questions on how to anticipate network-threatening > DDOS attacks, such as those coming from the Mirai botnet family, using > ML? Even going as far as designing and standardising an efficient, > secure network protocol for IoT devices. This is a complicated issue, > which involves emerging markets, so I'm interested in useful ideas > from any angle. > Regarding DDoS, I think we have to consider two angles even when considering protecting the network. The flooding-like attacks (1) and the most sophisticated attack that can highly degrade service performance even with few packets (2) (and of course this also concerns network service, even more than before with network softwarization which puts network functions into VM, being thus more exposed than before in my opinion). For type 1, there are usually observable signs of a future attack (without ML) in the hours or days before with an increasing load. However even if you observe it, it is very hard to fully characterize it and so prevent it to really happen. For type 2, you basically need to predict what should be the load induced by a packet or flow when considering the targeted service. Assuming you have no complete knwoledge of the service, you can try to use some learning and regression techniques to do it but assuming also that the trafic is encrypted, the problem becomes more difficult. jerome > Thanks, and I'm glad to meet you all. > > Best regards, > Brian Muhia. > > > On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, > > wrote: > > Hi, all, > > If there is a meeting, I would like to participate remotely. > > Best regards, > > Oscar > > On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang > > wrote: > > Hi, all, > > Although there are many understanding for Intelligence-Defined > Network, we are actually using this IDN as a term reference to > the SDN-beyond architecture that we presented in IETF97, see > the below link. A reference model is presented in page 3, > while potential standardization works is presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-inte= lligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in > network giving the recent story of the concluded proposed > NMLRG, we still would like to call for interests in IDN. > Anybody (on site in Chicago this week) are interested in this > or even wider topics regarding to AI/ML in network, please > contact me on jiangsheng@huawei.com > . Then we may have an informal > meeting to discuss some common interests and potential future > activities (not any activities in IETF, but also other STO or > experimental trails, etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the > ETSI NGP (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?= WKI_ID=3D51011 > > Meanwhile, please do use this mail list as a forum to discuss > any topics that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > --=20 > *Oscar Mauricio Caicedo Rend=C3=B3n* > *PhD Computer Science - Federal University of Rio Grande do Sul* > *Full Profesor - University of Cauca* > > -------------------------------------------------------------------= ----- > * > */**/Universidad del Cauca: Comprometidos con la calidad/**/.* > *_______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --=20 > Some say he really tries to learn efficiently. > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --------------FB54D18EFA9058C72F5D358D Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: 8bit Hi,

Le 28/03/2017 à 20:06, Brian Njenga a écrit :

I'm glad that the discussions​ on how to make AI/ML useful in the design of the internet's architecture are continuing, albeit under a different name. I'd like to participate remotely as well.

I have a question that someone more thoughtful​ than I could answer: Are there research questions on how to anticipate network-threatening DDOS attacks, such as those coming from the Mirai botnet family, using ML? Even going as far as designing and standardising an efficient, secure network protocol for IoT devices. This is a complicated issue, which involves emerging markets, so I'm interested in useful ideas from any angle.

Regarding DDoS, I think we have to consider two angles even when considering protecting the network. The flooding-like attacks (1) and the most sophisticated attack that can highly degrade service performance even with few packets (2) (and of course this also concerns network service, even more than before with network softwarization which puts network functions into VM, being thus more exposed than before in my opinion).

For type 1, there are usually observable signs of a future attack (without ML) in the hours or days before with an increasing load. However even if you observe it, it is very hard to fully characterize it and so prevent it to really happen. For type 2, you basically need to predict what should be the load induced by a packet or flow when considering the targeted service. Assuming you have no complete knwoledge of the service, you can try to use some learning and regression techniques to do it but assuming also that the trafic is encrypted, the problem becomes more difficult.

jerome

Thanks, and I'm glad to meet you all.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, <omcaicedo@unicauca.edu.co> wrote:
Hi, all,

If there is a meeting, I would like to participate remotely.

Best regards,

Oscar

On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang <jiangsheng@huawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we are actually using this IDN as a term reference to the SDN-beyond architecture that we presented in IETF97, see the below link. A reference model is presented in page 3, while potential standardization works is presented in page 9.

https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the recent story of the concluded proposed NMLRG, we still would like to call for interests in IDN. Anybody (on site in Chicago this week) are interested in this or even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawei.com . Then we may have an informal meeting to discuss some common interests and potential future activities (not any activities in IETF, but also other STO or experimental trails, etc.)  on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Next Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844
https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=51011

Meanwhile, please do use this mail list as a forum to discuss any topics that may applying AI/ML into network area.

Best regards,

Sheng
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--
Oscar Mauricio Caicedo Rendón
PhD Computer Science - Federal University of Rio Grande do Sul
Full Profesor - University of Cauca

Universidad del Cauca: Comprometidos con la calidad.
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https://www.ietf.org/mailman/listinfo/idnet
--
Some say he really tries to learn efficiently.


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--------------FB54D18EFA9058C72F5D358D-- From nobody Tue Mar 28 11:29:07 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 3A2B11299C8 for ; Tue, 28 Mar 2017 11:29:05 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id kqPUh8b2TjYb for ; Tue, 28 Mar 2017 11:29:02 -0700 (PDT) Received: from mail-qk0-x22e.google.com (mail-qk0-x22e.google.com [IPv6:2607:f8b0:400d:c09::22e]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 145C6129A07 for ; Tue, 28 Mar 2017 11:28:48 -0700 (PDT) Received: by mail-qk0-x22e.google.com with SMTP id d10so67452896qke.1 for ; Tue, 28 Mar 2017 11:28:48 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=Klk9NaQJ17C/XsJbf3bc3vMnS5tyNV2A54l/4eoaqzw=; b=aUTr32l9FJIGAyMInNVym0AAWhgbxdN/BYC+Sx6siMaesq/xa+9rnUe2qXhX7gTI7c rlDe01hbXoGKv2Sf9ZYExJzKuLlnoySsgWa/2OiBlBKjdPGbNiXcQlj/0JR6PLf4yuZu YSvQz7Tjl8gBOs33fVxyn78rynVZYe11EeNsJs4S7bAefQHTZb5/Nqwqb2nMMsAf8tjZ 9esv+ZAdJS9x7zrxD78nS6PL4sDOCAw41GrivBf9HlGjO0h+SPrNNebuglto/h7fIcFE cnnaMErZgSDqqsU/PQqBa2UldJw9UlbLH9KW/q0iEg6fahLWo5kQcQaGOUdJ8mYJEoCu +J6A== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=Klk9NaQJ17C/XsJbf3bc3vMnS5tyNV2A54l/4eoaqzw=; b=Fnxi8tqCB5eDC23QOUCXisARTIL2xypkYprMYm0QgwevSvN5UKYht5n3UA6jOqUT0N PUe5kIQ3hD6DSqaVkD/Dp+VQtGdZ6LdRIWSkU+pm/iCKVk0D9oQTaPC0OSFtQe3XiPhf Oklxw3/JgxQaNudulMTHbWMSLUJEYJlCLOG4YOwWkXcHMWjLEYQgw/7KxEx6tWQI14Tj L8kEzlZF6rwfwyghTeFeVyJ6NJy1PTbBfUGvXnmhu6DBInWbMmnG9HBMKJfj7JWFQikN IDWNEHJjfnQEw2Af+hUH79BPXGQ0vpxES4NcRgojzE+qRAq9T5MMGJ5z2tJyz7c0hQYT D/jA== X-Gm-Message-State: AFeK/H0n2jg6tqLzZ4S15iHRUpx1nlH6Bt0q7wiG7XgS+l8UrD04Le8DGY8MBzPaFtHQbJOaaeIQbDtf1LjrRw== X-Received: by 10.55.200.88 with SMTP id c85mr26170720qkj.155.1490725727123; Tue, 28 Mar 2017 11:28:47 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 11:28:46 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: David Meyer Date: Tue, 28 Mar 2017 11:28:46 -0700 Message-ID: To: Brian Njenga Cc: Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a11433d0e6c552c054bcea159 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:29:05 -0000 --001a11433d0e6c552c054bcea159 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Brian, Fortunately for us, there is tons of tutorial material (and code) around the network. Distill is a cool idea, but there are many more. Dave On Tue, Mar 28, 2017 at 11:26 AM, Brian Njenga wrote: > Thanks, Dave. I'll check them out. > > Something I currently think is very cool is the Distill journal, at > https://distill.pub, edited by Shan Carter and Chris Olah. > > I would love to see more ML tutorials=E2=80=8B and lessons written in tha= t style. > They're=E2=80=8Baccepting submissions for tutorials, I think, so if any M= L expert > here would like to explain any technique in an explorable manner, please > reach out to them. > > On Tue, 28 Mar 2017, 21:17 David Meyer, wrote: > >> Hey Brian, >> >> We are working on anomaly detection technologies to detect things like >> DDOS attacks; we're about to publish some results and will let you know >> when that is ready. In the mean time, you might want to look at attacks >> against ML itself, see [0], [1], and [2]. The bottom line here is that >> even simple linear models are susceptible to adversarial attacks, so thi= ngs >> like the autoencoders we used to do binary classification (e.g., anomaly >> detection) are susceptible; see slides 16+ of [3]. >> >> Dave >> >> [0] https://arxiv.org/pdf/1312.6199.pdf >> [1] https://arxiv.org/pdf/1412.6572.pdf >> [2] https://arxiv.org/pdf/1602.02697.pdf >> [3] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pdf >> >> On Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga >> wrote: >> >> I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in th= e design >> of the internet's architecture are continuing, albeit under a different >> name. I'd like to participate remotely as well. >> >> I have a question that someone more thoughtful=E2=80=8B than I could ans= wer: Are >> there research questions on how to anticipate network-threatening DDOS >> attacks, such as those coming from the Mirai botnet family, using ML? Ev= en >> going as far as designing and standardising an efficient, secure network >> protocol for IoT devices. This is a complicated issue, which involves >> emerging markets, so I'm interested in useful ideas from any angle. >> >> Thanks, and I'm glad to meet you all. >> >> Best regards, >> Brian Muhia. >> >> On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, < >> omcaicedo@unicauca.edu.co> wrote: >> >> Hi, all, >> >> If there is a meeting, I would like to participate remotely. >> >> Best regards, >> >> Oscar >> >> On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang >> wrote: >> >> Hi, all, >> >> Although there are many understanding for Intelligence-Defined Network, >> we are actually using this IDN as a term reference to the SDN-beyond >> architecture that we presented in IETF97, see the below link. A referenc= e >> model is presented in page 3, while potential standardization works is >> presented in page 9. >> >> https://www.ietf.org/proceedings/97/slides/slides- >> 97-nmlrg-intelligence-defined-network-01.pdf >> >> Although it might be a little bit too early for AI/ML in network giving >> the recent story of the concluded proposed NMLRG, we still would like to >> call for interests in IDN. Anybody (on site in Chicago this week) are >> interested in this or even wider topics regarding to AI/ML in network, >> please contact me on jiangsheng@huawei.com . Then we may have an >> informal meeting to discuss some common interests and potential future >> activities (not any activities in IETF, but also other STO or experiment= al >> trails, etc.) on Thursday morning. >> >> FYI, we have already working on a Work Item, called IDN in the ETSI NGP >> (Next Generation Protocol) ISG, links below. >> >> https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 >> https://portal.etsi.org/webapp/WorkProgram/Report_ >> WorkItem.asp?WKI_ID=3D51011 >> >> Meanwhile, please do use this mail list as a forum to discuss any topics >> that may applying AI/ML into network area. >> >> Best regards, >> >> Sheng >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> >> >> >> -- >> *Oscar Mauricio Caicedo Rend=C3=B3n* >> *PhD Computer Science - Federal University of Rio Grande do Sul* >> *Full Profesor - University of Cauca* >> >> ------------------------------ >> *Universidad del Cauca: Comprometidos con la calidad.* >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> -- >> Some say he really tries to learn efficiently. >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> >> -- > Some say he really tries to learn efficiently. > --001a11433d0e6c552c054bcea159 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Brian,

Fortunately for us, there is ton= s of tutorial material (and code) around the network. Distill is a cool ide= a, but there are many more.

Dave


On Tue, = Mar 28, 2017 at 11:26 AM, Brian Njenga <iambrianmuhia@gmail.com&= gt; wrote:

Thanks, = Dave. I'll check them out.

Something I currently think is very cool is the Distill jour= nal, at https://distill.p= ub, edited by Shan Carter and Chris Olah.

I would love to see more ML tutorials=E2=80=8B and lessons w= ritten in that style. They're=E2=80=8Baccepting submissions for tutoria= ls, I think, so if any ML expert here would like to explain any technique i= n an explorable manner, please reach out to them.

=

On Tue, 28 Mar 2017, 21:17 = David Meyer, <dmm@1-4= -5.net> wrote:
Hey Brian,

<= /div>
We are working on anoma= ly detection technologies to detect things like DDOS attacks; we're abo= ut to publish some results and will let you know when that is ready. In the= mean time, you might want to look at attacks against ML itself, see [0], [= 1], and [2].=C2=A0 The bottom line here is that even simple linear models a= re=C2=A0susceptible to adversarial attacks, so things like the autoencoders= we used to do binary classification (e.g., anomaly detection) are=C2=A0sus= ceptible; see slides 16+ of [3].

Dave

[1]=C2=A0http= s://arxiv.org/pdf/1412.6572.pdf
[2]=C2=A0https://arxiv.= org/pdf/1602.02697.pdf
[3]=C2=A0http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pdf
On Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga = <iambrianmuhia@gmail.com> wrote:

I'm glad that the discussions=E2=80=8B on how to make AI/ML useful i= n the design of the internet's architecture are continuing, albeit unde= r a different name. I'd like to participate remotely as well.

I have a question = that someone more thoughtful=E2=80=8B than I could answer: Are there resear= ch questions on how to anticipate network-threatening DDOS attacks, such as= those coming from the Mirai botnet family, using ML? Even going as far as = designing and standardising an efficient, secure network protocol for IoT d= evices. This is a complicated issue, which involves emerging markets, so I&= #39;m interested in useful ideas from any angle.

Thanks, and I'= ;m glad to meet you all.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, <= ;omcaicedo@unicauca.edu.co> wrote:
Hi, all,

If there is a meeting, I would l= ike to participate remotely.
<= br class=3D"m_-8459179442773682101m_-3993023778041450664m_35145464417528785= 14gmail_msg m_-8459179442773682101gmail_msg">
Best regards,

Oscar

= On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang <jiangsheng@hua= wei.com> wrote:
=
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https://www.ietf.org/proceedings/9= 7/slides/slides-97-nmlrg-intelligence-defined-network-01.pdf<= br class=3D"m_-8459179442773682101m_-3993023778041450664m_35145464417528785= 14gmail_msg m_-8459179442773682101gmail_msg">
Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may ha= ve an informal meeting to discuss some common interests and potential futur= e activities (not any activities in IETF, but also other STO or experimenta= l trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844
https://portal.etsi.org/webapp/WorkProgram/Report_<= wbr>WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.iet= f.org/mailman/listinfo/idnet



--
Oscar Mauricio = Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0Federal University of Rio Grande do Sul
Full Profesor - University of Cauca

Universidad del Cauca: Comprometidos con la calidad= .
________________________________________= _______
IDNET mailing list
IDNET@ietf.org
https://www.iet= f.org/mailman/listinfo/idnet
--
Some say he re= ally tries to learn efficiently.

_____________________________= __________________
IDNET mailing list
IDNET@ietf.org
https://www.iet= f.org/mailman/listinfo/idnet


--
Some say he really tries to learn efficientl= y.

--001a11433d0e6c552c054bcea159-- From nobody Tue Mar 28 11:29:38 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 4F5A2129559 for ; Tue, 28 Mar 2017 11:29:37 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.902 X-Spam-Level: X-Spam-Status: No, score=-1.902 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id yhXHqD2WFVMm for ; Tue, 28 Mar 2017 11:29:34 -0700 (PDT) Received: from ns1.nict.go.jp (ns1.nict.go.jp [IPv6:2001:df0:232:300::1]) by ietfa.amsl.com (Postfix) with ESMTP id 560F31298D3 for ; Tue, 28 Mar 2017 11:29:30 -0700 (PDT) Received: from gw1.nict.go.jp (gw1.nict.go.jp [133.243.18.250]) by ns1.nict.go.jp with ESMTP id v2SITTlT054895 for ; Wed, 29 Mar 2017 03:29:29 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw1.nict.go.jp with ESMTP id v2SITShj054892 for ; Wed, 29 Mar 2017 03:29:28 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id 58EB8A127 for ; Wed, 29 Mar 2017 03:29:28 +0900 (JST) Date: Wed, 29 Mar 2017 03:29:21 +0900 From: Pedro Martinez-Julia To: list-idnet Message-ID: <20170328182921.GQ4808@spectre> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith1 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:29:37 -0000 Dear David, You did it again: You wrote a quite interesting paragraph and well supported by references. Would you give me permission to copy the text, and possibly the references, to the Git repository? Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Tue Mar 28 11:32:56 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 8E2D11297CF for ; Tue, 28 Mar 2017 11:32:54 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id RaHk6PVTsvEY for ; Tue, 28 Mar 2017 11:32:52 -0700 (PDT) Received: from mail-qt0-x22e.google.com (mail-qt0-x22e.google.com [IPv6:2607:f8b0:400d:c0d::22e]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D79EF1298D3 for ; Tue, 28 Mar 2017 11:32:49 -0700 (PDT) Received: by mail-qt0-x22e.google.com with SMTP id i34so71991091qtc.0 for ; Tue, 28 Mar 2017 11:32:49 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=byPoYwHwytANxuBXxVRBr7+ON2JLDgbA+Zs1MvIljTs=; b=D555ejmdjtLv4SG2T1U4afedLk85x8NW5ojRd+Qpg7fbWJQHOb4ouaI2DEn6XMVvkQ 1/YnF7hs0VpSckoGrl1DcmlcYI2QK40LpMvTowt3wzSXFOIX0rXP6Ji1eXv1SSlXxLSY 0QMsZfyXmVcEmm7OFMQETA1FeP2uzWm9uyUWdMQGHgWPaks+r84ceAa/I+56YOwA47xR PTeO0LXpyqeT4DjkSpn6JYQLcGJcUXQY8Kd/Oj64vpotp5VlEz+m4RnkcUwM6TpVUoox wOtBJobTi7jA0ufSXb3OrAc0Hh5VyBfjUUn31FJKYwBo64Tr+YjTRXMt/kNmMoL49CgW SWsg== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=byPoYwHwytANxuBXxVRBr7+ON2JLDgbA+Zs1MvIljTs=; b=HfrYSE/pxIe/1+uP5Mxj9TqWF9aqIR50vDa043kPhbwvWmOnho+PEEhBYcMKaDG3RP Z36ZR+8icxH5f4QUsjH6r1p+lUwG1inArREfkTPZmikOB5GZk56qfqrzP5Txp7jZEtzk U98ds0C4pd1JfuclaQSoCDW5lQh43rQD9u05vXS1J9yaVjPtrjSEX4Kexymu89NNCqJT IQONISEDjuZuu9FJuVWYI6fnmME0Z5nHasLzknqiGYxrljQ/sR1d8N4AH+Y72GWp51es XoMp/beEyPgUNHhSODeriAa6swOFFWhwA0gbXCU6Osyt33LRkzcu69VJf/844A91pC/3 +EhA== X-Gm-Message-State: AFeK/H0ZI+nk15svw7q6HfV1DIRlOsqhopHu/Bk+G7oeHCppL1Yf0LgjCm9ux6hjM9PdgJKTzar/KgupW+rCTw== X-Received: by 10.200.55.57 with SMTP id o54mr30538106qtb.78.1490725968846; Tue, 28 Mar 2017 11:32:48 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 11:32:48 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <20170328182921.GQ4808@spectre> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <20170328182921.GQ4808@spectre> From: David Meyer Date: Tue, 28 Mar 2017 11:32:48 -0700 Message-ID: To: Pedro Martinez-Julia Cc: list-idnet Content-Type: multipart/alternative; boundary=001a113c036ed4b9be054bceaf90 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:32:55 -0000 --001a113c036ed4b9be054bceaf90 Content-Type: text/plain; charset=UTF-8 Sure, be my guest. Dave On Tue, Mar 28, 2017 at 11:29 AM, Pedro Martinez-Julia wrote: > Dear David, > > You did it again: You wrote a quite interesting paragraph and well > supported by references. Would you give me permission to copy the text, > and possibly the references, to the Git repository? > > Regards, > Pedro > > -- > Pedro Martinez-Julia > Network Science and Convergence Device Technology Laboratory > Network System Research Institute > National Institute of Information and Communications Technology (NICT) > 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan > Email: pedro@nict.go.jp > --------------------------------------------------------- > *** Entia non sunt multiplicanda praeter necessitatem *** > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a113c036ed4b9be054bceaf90 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Sure, be my guest. Dave


On Tue, Mar 28, 2017 at 11:29 A= M, Pedro Martinez-Julia <pedro@nict.go.jp> wrote:
Dear David,

You did it again: You wrote a quite interesting paragraph and well
supported by references. Would you give me permission to copy the text,
and possibly the references, to the Git repository?

Regards,
Pedro

--
Pedro Martinez-Julia
Network Science and Convergence Device Technology Laboratory
Network System Research Institute
National Institute of Information and Communications Technology (NICT)
4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Email: pedro@nict.go.jp
---------------------------------------------------------
*** Entia non sunt multiplicanda praeter necessitatem ***

____________________________= ___________________
IDNET mailing list
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https://www.ietf.org/mailman/listinfo/idnet

--001a113c036ed4b9be054bceaf90-- From nobody Tue Mar 28 11:38:41 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 649F31299D3 for ; Tue, 28 Mar 2017 11:38:40 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 5Z58xtPQwwDr for ; Tue, 28 Mar 2017 11:38:37 -0700 (PDT) Received: from mail-oi0-x229.google.com (mail-oi0-x229.google.com [IPv6:2607:f8b0:4003:c06::229]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 15BFF1299E8 for ; Tue, 28 Mar 2017 11:38:36 -0700 (PDT) Received: by mail-oi0-x229.google.com with SMTP id o67so33930467oib.1 for ; Tue, 28 Mar 2017 11:38:36 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:references:in-reply-to:from:date:message-id:subject:to :cc; bh=n0fimOJuQLRJfgRyRS2se6okFmT3U4c7uU0/x9CXzCM=; b=RY5OkRX3Hr4W/ubIsNTnh5qe3Q7M99zQD1cNhtklTdvV1rHJsM9ZudLq+MsW75lzqs HFBOBXQcifbYLvhoLAcEjNHwkQlKFHeBBi6sShA/Q8dnPlRamNJlQ4L8NryC5uIFCoEF UOLHmTeVkfurg6yEH67QO4udj9SM8RH4haCl6pNBb4JNdDpqW8pTthUoemsWN9yfbBfP O9GJejL/kfyyjRRqyZrwdR80X/G7fBd+BFnVPf00m1wgKVTbTLA2n+SVddvp+fxY6a9A 77OYPhS/fl4ggrpw3GlR8K+cv8aE8PtVq3L9bK/lWsuzkTb9XAoR94NJbhKGagJf6RE+ LtOA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:references:in-reply-to:from:date :message-id:subject:to:cc; bh=n0fimOJuQLRJfgRyRS2se6okFmT3U4c7uU0/x9CXzCM=; b=eCvBScLab8m27tSXY2cGnk9q44jRlsYQUd/leC1jvv4u8Q5dg95MBjlDZ5SnQNOM6g GfvUrmj4Y6ezSm5zRsjR08nXPh5kBgooEjcN6SkWE8mFuDIApz0wmKSGblRyR42nIrdt VjiKyjJLd/tNHxjFbKEq7V9E7okZjV1EVBcMrpc+DNIIPc6Hle9hVhkf8QeSG6TeYPlL hphrqiKgfA5aytkQ/0K5aBd0+p/eFWw3QKdXclURDDGe5qjh1sRypseij6tlLYfrrnTQ ZPzi0k6Opaws5E5cmw+wjugT3kisBQl+WQ2XbB/8X4IcitlSDUALV7L8EIqT0vYlGg2r PvTg== X-Gm-Message-State: AFeK/H0oMrzbn+a+B73eE/o/YibdAunFk7kZZduAw+p0VTLyr22Jo5MJdtV9nULKymF+KCvQiRVLQUzpi8TB9g== X-Received: by 10.202.225.11 with SMTP id y11mr199528oig.66.1490726310375; Tue, 28 Mar 2017 11:38:30 -0700 (PDT) MIME-Version: 1.0 References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: From: Brian Njenga Date: Tue, 28 Mar 2017 18:38:20 +0000 Message-ID: To: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Oscar Mauricio Caicedo Rendon , Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a113ce25c2ff588054bcec4c0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:38:40 -0000 --001a113ce25c2ff588054bcec4c0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Got it, thanks! Great response. What other research questions do people have? On Tue, 28 Mar 2017, 21:26 J=C3=A9r=C3=B4me Fran=C3=A7ois, wrote: > Hi, > > > Le 28/03/2017 =C3=A0 20:06, Brian Njenga a =C3=A9crit : > > I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in the= design > of the internet's architecture are continuing, albeit under a different > name. I'd like to participate remotely as well. > > I have a question that someone more thoughtful=E2=80=8B than I could answ= er: Are > there research questions on how to anticipate network-threatening DDOS > attacks, such as those coming from the Mirai botnet family, using ML? Eve= n > going as far as designing and standardising an efficient, secure network > protocol for IoT devices. This is a complicated issue, which involves > emerging markets, so I'm interested in useful ideas from any angle. > > Regarding DDoS, I think we have to consider two angles even when > considering protecting the network. The flooding-like attacks (1) and the > most sophisticated attack that can highly degrade service performance eve= n > with few packets (2) (and of course this also concerns network service, > even more than before with network softwarization which puts network > functions into VM, being thus more exposed than before in my opinion). > > For type 1, there are usually observable signs of a future attack (withou= t > ML) in the hours or days before with an increasing load. However even if > you observe it, it is very hard to fully characterize it and so prevent i= t > to really happen. For type 2, you basically need to predict what should b= e > the load induced by a packet or flow when considering the targeted servic= e. > Assuming you have no complete knwoledge of the service, you can try to us= e > some learning and regression techniques to do it but assuming also that t= he > trafic is encrypted, the problem becomes more difficult. > > jerome > > > Thanks, and I'm glad to meet you all. > > Best regards, > Brian Muhia. > > On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, < > omcaicedo@unicauca.edu.co> wrote: > > Hi, all, > > If there is a meeting, I would like to participate remotely. > > Best regards, > > Oscar > > On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang > wrote: > > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, w= e > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-d= efined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D5= 1011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > -- > *Oscar Mauricio Caicedo Rend=C3=B3n* > *PhD Computer Science - Federal University of Rio Grande do Sul* > *Full Profesor - University of Cauca* > > ------------------------------ > * Universidad del Cauca: Comprometidos con la calidad. * > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > -- > Some say he really tries to learn efficiently. > > > _______________________________________________ > IDNET mailing listIDNET@ietf.orghttps://www.ietf.org/mailman/listinfo/idn= et > > > -- Some say he really tries to learn efficiently. --001a113ce25c2ff588054bcec4c0 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

Got it, thanks! Great response. What other research question= s do people have?


On Tue, 28 Mar 2017, 21:26 = J=C3=A9r=C3=B4me Fran=C3=A7ois, <jerome.francois@inria.fr> wrote:
=20 =20 =20
Hi,
=

Le 28/03= /2017 =C3=A0 20:06, Brian Njenga a =C3=A9crit=C2=A0:

I'm glad that the discussions= =E2=80=8B on how to make AI/ML useful in the design of the internet's architecture are continuing, albeit under a different name. I'd like to participate remotely as well.

I have a question that someone mor= e thoughtful=E2=80=8B than I could answer: Are there research questions on how to anticipate network-threatening DDOS attacks, such as those coming from the Mirai botnet family, using ML? Even going as far as designing and standardising an efficient, secure network protocol for IoT devices. This is a complicated issue, which involves emerging markets, so I'm interested in useful ideas from any angle.

Regarding DDoS, I think we have to consider two angles even when considering protecting the network. The flooding-like attacks (1) and the most sophisticated attack that can highly degrade service performance even with few packets (2) (and of course this also concerns network service, even more than before with network softwarization which puts network functions into VM, being thus more exposed than before in my opinion).

For type 1, there are usually observable signs of a future attack (without ML) in the hours or days before with an increasing load. However even if you observe it, it is very hard to fully characterize it and so prevent it to really happen. For type 2, you basically need to predict what should be the load induced by a packet or flow when considering the targeted service. Assuming you have no complete knwoledge of the service, you can try to use some learning and regression techniques to do it but assuming also that the trafic is encrypted, the problem becomes more difficult.

jerome


Thanks, and I'm glad to meet = you all.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 Osc= ar Mauricio Caicedo Rendon, <omcaicedo@unicauca.edu.co> wrote:
Hi, all,

If there is a meeting, I would like to participate remotely.

Best regards,

Oscar

On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang &= lt;jiangsheng@huawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we are actually using this IDN as a term reference to the SDN-beyond architecture that we presented in IETF97, see the below link. A reference model is presented in page 3, while potential standardization works is presented in page 9.

https://www.ietf.org/proceedings/97/slides/sl= ides-97-nmlrg-intelligence-defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the recent story of the concluded proposed NMLRG, we still would like to call for interests in IDN. Anybody (on site in Chicago this week) are interested in this or even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawe= i.com . Then we may have an informal meeting to discuss some common interests and potential future activities (not any activities in IETF, but also other STO or experimental trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Next Generation Protocol) ISG, links below.

https:= //portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844
https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp= ?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics that may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/= mailman/listinfo/idnet



--
O= scar Mauricio Caicedo Rend=C3=B3n
PhD Compute= r Science -=C2=A0Federal University of Rio Grande do Sul
Full Profesor - University of Cauca

Universidad del Cauca: Comprometidos con la calidad= .
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailma= n/listinfo/idnet
--
Some say he really tries to le= arn efficiently.


____________________________________________=
___
IDNET mailing list
IDNET@ietf.org
https://www.=
ietf.org/mailman/listinfo/idnet

--
Some say he really tries to learn ef= ficiently.
--001a113ce25c2ff588054bcec4c0-- From nobody Tue Mar 28 11:46:53 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 3D7DD1299F7 for ; Tue, 28 Mar 2017 11:46:51 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.921 X-Spam-Level: X-Spam-Status: No, score=-6.921 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_HI=-5, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id H2DDGxfMCWYX for ; Tue, 28 Mar 2017 11:46:49 -0700 (PDT) Received: from mail3-relais-sop.national.inria.fr (mail3-relais-sop.national.inria.fr [192.134.164.104]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id ED9201270FC for ; Tue, 28 Mar 2017 11:46:48 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.36,237,1486422000"; d="scan'208";a="218387763" Received: from dhcp-8907.meeting.ietf.org (HELO [31.133.139.7]) ([31.133.139.7]) by mail3-relais-sop.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 28 Mar 2017 20:46:46 +0200 To: idnet@ietf.org References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <20170328182530.GP4808@spectre> From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: <84ff06ac-61e7-3e71-f6f9-c78335c69aaf@inria.fr> Date: Tue, 28 Mar 2017 20:46:44 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <20170328182530.GP4808@spectre> Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:46:51 -0000 Hi Le 28/03/2017 =E0 20:25, Pedro Martinez-Julia a =E9crit : > On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote: >> Hey Sheng, >> >> I just wanted to revive my key concern on [0] (same one I made at the >> NMRL): The hard parts of getting Machine Learning intelligence into >> Networking is the Machine Learning part. In addition, successful deplo= yment >> of ML requires knowledge of ML combined with domain knowledge. We >> definitely have the domain knowledge; the problem is that we don't hav= e the >> ML knowledge, and this is one of the big factors holding us back; see = e.g. >> Andrew's discussion of talent in [1]. Slides such as [0] seem to impl= y >> that *someone else* (in particular, not us) will handle the ML part o= f all >> of this. I'll just note that in general successful deployments of ML d= on't >> work this way; the domain experts will have to learn ML (and vice vers= a) >> for us to be successful (again, see [1] and many others). > Dear Dave, > > You are true in that ML/domain knowledge is necessary but, however it i= s > worth to take into account that it is not strictly required and it will= > even be counterproductive in some (or maybe most) situations. At the en= d > of the day, encouraging (or forcing) a network expert to learn ML is > quite difficult, the results will be delayed until the learning phase > ends, and (most probably) s/he will never get a better solution than a > person that has been an expert in ML from a long time ago. Therefore, i= t > is better to make separate experts (in ML and the domain itself) to > collaborate in a common solution. Therefore, and I think it has been > mentioned before, we have to (try to) enroll experts in ML to the IDNET= > group and see what can we do together... This is a general trend that only a single person cannot be expert in everything. Actually, a good network expert may require good ML but also good software skills (including software formal verification knowledge). So, in my opinion the problem is larger. Enhancing collaboration between network and ML expert is a path that starts in many company and insitutes I think. Discussing with ML experts, they are usually open and happy to discover new "use cases"=20 but their first question will be "do you have some labelled datasets that we can work with" which relates to the problem raised in previous emails about open datasets. In my opinion, if we wan to attract ML experts in our dicussions, we should identify few scenrios, defined them precisely and provide an open dataset. By defining them, I mean we have to give them all background they need to understand (that can be built incrementally through discussion) in a well-documented format. jerome >> Perhaps a useful exercise would be to write an ID that makes your >> assumptions explicit? >> >> Thanks, >> Dave > Regards, > Pedro > From nobody Tue Mar 28 16:04:47 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 8A2B1129546 for ; Tue, 28 Mar 2017 16:04:46 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.901 X-Spam-Level: X-Spam-Status: No, score=-1.901 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id c1g-fzVf-GqF for ; Tue, 28 Mar 2017 16:04:36 -0700 (PDT) Received: from smtpeg.etri.re.kr (smtpeg2.etri.re.kr [129.254.27.142]) (using TLSv1 with cipher AES128-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id BD998128E19 for ; Tue, 28 Mar 2017 16:04:35 -0700 (PDT) Received: from SMTP2.etri.info (129.254.28.72) by SMTPEG2.etri.info (129.254.27.142) with Microsoft SMTP Server (TLS) id 14.3.319.2; Wed, 29 Mar 2017 08:04:33 +0900 Received: from SMTP1.etri.info ([169.254.1.111]) by SMTP2.etri.info ([169.254.2.142]) with mapi id 14.03.0319.002; Wed, 29 Mar 2017 08:04:29 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: Brian Njenga , =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , "Oscar Mauricio Caicedo Rendon" , Sheng Jiang CC: "idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGp43QAgAAR6oCAAAWvAIAAA0cAgADS4nw= Date: Tue, 28 Mar 2017 23:04:29 +0000 Message-ID: <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> , In-Reply-To: Accept-Language: ko-KR, en-US Content-Language: ko-KR X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [129.254.28.42] Content-Type: multipart/alternative; boundary="_000_5BC916BD50F92F45870ABA46212CB29CE4C966SMTP1etriinfo_" MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 23:04:46 -0000 --_000_5BC916BD50F92F45870ABA46212CB29CE4C966SMTP1etriinfo_ Content-Type: text/plain; charset="utf-8" Content-Transfer-Encoding: base64 SGkgQnJpYW4sDQoNCg0KQXMgeW91IG1lbnRpb25lZCBieSB0aGUgcHJpb3IgZW1haWwsIGFudGlj 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AQHSp96NotmNK6LAE0GQptHszQd4taGqd3NwgACYo+A= Date: Wed, 29 Mar 2017 01:59:51 +0000 Message-ID: <3B110B81B721B940871EC78F107D848CF32D50@DGGEMM506-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.134.134.227] Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090206.58DB1524.0080, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.4.150, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 9e89b901dace9220ddab250b0630f8bd Archived-At: Subject: [Idnet] =?gb2312?b?tPC4tDogSW50ZWxsaWdlbmNlLURlZmluZWQgTmV0d29y?= =?gb2312?b?ayBBcmNoaXRlY3R1cmUgYW5kIENhbGwgZm9yIEludGVyZXN0cw==?= X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: 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HTTP; Wed, 29 Mar 2017 07:17:08 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> From: David Meyer Date: Wed, 29 Mar 2017 07:17:08 -0700 Message-ID: To: =?UTF-8?B?6rmA66+87ISd?= Cc: Brian Njenga , =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/mixed; boundary=001a113fed40749c58054bdf3bbd Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 14:17:19 -0000 --001a113fed40749c58054bdf3bbd Content-Type: multipart/alternative; boundary=001a113fed40749c54054bdf3bbb --001a113fed40749c54054bdf3bbb Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Min-Suk, Totally agree we need to learn from our environment, and RL is a natural approach. After all, the network is always changing, has adversaries, etc. All of this means. among other things, that we can't make simplifying assumptions like stationary distributions, iid data, .... So RL is one way to attack these problems, and the classic algorithms you mention below are certainly a reasonable approach (I've been working with policy gradients [0], trying to model/adapt the two-player game approach of AlphaGo to networking; the problem there is that we don't have a source of labeled expert data like the KGS Go server (https://www.gokgs.com/) to build the supervised policy network....). You might also want to check out the recent "boot" of evolution strategies as a black-box approach to RL (in particular no gradients). See [1], [2], [3]. There is also a ton of code around if you want to try some of this out (see e.g.,https://github.com/dennybritz/reinforcement-learning; this one is in tensorflow). Finally, I've attached a few summary slides with some of my musings on this topic from past talks. Thanks, Dave [BTW, two player minimax games seem to be popping up everywhere: AlphaGo, variational autoencoders [4], GANs [5], and many others; something to thing about for our domain] [0] https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement= -learning-with-function-approximation.pdf [1] https://blog.openai.com/evolution-strategies/ [2] https://arxiv.org/pdf/1703.03864.pdf [3] http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pdf [4] http://www.1-4-5.net/~dmm/ml/vae.pdf [5] https://arxiv.org/pdf/1406.2661.pdf On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: > Hi Brian, > > > As you mentioned by the prior email, anticipating network DDos > attacks is really trendy issue to solve by ML techniques. > > We also make some efforts how to avoid fagile nodes by a trustworthy > communication, that means quantifying trustworthiness of node with > normalization of various requirements such as security function, bandwidt= h > and etc. > > We are freshly approaching in routing layer with confidence using our own > requirements, TPD(Trust Policy Distribution) and TD(Trust Degree). These > requirements are considered to be solved by Reinforcement Learning > (RL) that is one of the ML algorithms. RL is useful to control some of > network policy about specific actions and states with reinforced and > purnished rewards (+/-), but the problem is too slow to acquire satisifie= d > performance. Other ways to say it, anormaly dectection and regression > analysis might be both efficient approaching methods to solve the issues > Dave mentioned. > > > Best Regards, > > > Min-Suk Kim > > Senior Researcher / Ph.D. > Intelligent IoE Network Research Section, > *E*lectronics and *T*elecommunications *R*esearch *I*nstitute (*ETRI)* > e-mail : mskim16@etri.re.kr > http://www.etri.re.kr/ > > > > > > > --001a113fed40749c54054bdf3bbb Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

Hey Min-Suk,

Totally agr= ee we need to learn from our environment, and RL is a natural approach. Aft= er all, the network is always changing, has adversaries, etc. All of this m= eans. among other things, =C2=A0that we can't make simplifying assumpti= ons like stationary distributions, =C2=A0iid data, .... So RL is one way to= attack these problems, and the classic algorithms you mention below are ce= rtainly a reasonable approach (I've been working with policy gradients = [0], trying to model/adapt the two-player game approach of AlphaGo to netwo= rking; the problem there is that we don't have a source of labeled expe= rt data like the KGS Go server (https://= www.gokgs.com/) to build the supervised policy network....).=C2=A0

You might also want to check out the recent "boot= " of evolution strategies as a black-box approach to RL (in particular= no gradients). See [1], =C2=A0[2], =C2=A0[3]. There is also a ton of code = around if you want to try some of this out (see e.g.,https://github.com/dennybritz/re= inforcement-learning; this one is in tensorflow). Finally, I've att= ached a few summary slides with some of my musings on this topic from past = talks.

Thanks,

Dave
=

[BTW, two player minimax games seem to be popping up ev= erywhere: AlphaGo, variational autoencoders [4], GANs [5], and many others;= something to thing about for our domain]

[5]=C2=A0https://arxiv.org/pdf/1406.2661.pdf

On Tue, Mar 28, 2017 at= 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D <mskim16@etri.re.kr> = wrote:

Hi Brian,


As you mentioned by the prior= email, anticipating network DDos attacks=C2=A0is=C2=A0really trendy issue = to solve by ML techniques.

We also make some efforts how= to=C2=A0avoid fagile nodes by=C2=A0a trustworthy communication, that means= quantifying trustworthiness of node=C2=A0with normalization=C2=A0of variou= s requirements such as security function,=C2=A0bandwidth and etc.

We are freshly approaching in= =C2=A0routing layer with confidence using our own requirements, TPD(Trust P= olicy Distribution) and TD(Trust Degree). These requirements are considered= to be solved by Reinforcement Learning (RL)=C2=A0that is one of the ML algorithms. RL is useful to control some o= f network=C2=A0policy about specific actions and states with reinforced and= purnished rewards (+/-), but the problem is too slow to=C2=A0acquire satis= ified performance. Other ways to=C2=A0say it,=C2=A0anormaly dectection and regression analysis=C2=A0might be=C2=A0both efficient=C2=A0= approaching methods to solve the issues Dave mentioned.


Best Regards,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Researcher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
E<= /strong>lectronics and=C2=A0Telecommunications=C2=A0Re= search=C2=A0Institute (ETRI)
e-mail =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0: =C2=A0mskim1= 6@etri.re.kr

=C2=A0

=C2=A0


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(128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id ED5E412953E for ; Wed, 29 Mar 2017 07:25:41 -0700 (PDT) Received: by mail-qk0-x235.google.com with SMTP id f11so14134622qkb.0 for ; Wed, 29 Mar 2017 07:25:41 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=0QuCB2iLvvf9ov41blPcxP6xkpe1WYAuN3V1iRU29PM=; b=R7OL4gRHIanb7ueUE2yJLlmQZ5LrQO+wPqU3dJItEKGxJlIo73uX5GIoG+VOv9HrwC i6A2M21xvJsa+JXXc0WITCm/1fUS+f8CE9obgK2GAfmA56K0+DSzzQHYEPTR5hWcX3r2 Th7Ax/9BKy2MIEiFO+B8OLxgT0B+CDUrl81w3+TIrWsDG6ov/6pOOZozae5opI/Q7i1x wNUkBzc7xIoTFpK6q00cirBMSsum2fdLrsgSjouic4Q4N76qjYXaDt9/28hT5Vfr93TW Am+6hh+SMRlFDOYwPRddscN+XAg+W9db3l0oJudW3BV5tcGjpkgEHbBG9hG89L60DpTK nIFg== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date 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To: =?UTF-8?B?6rmA66+87ISd?= Cc: Brian Njenga , =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a114ac54edcf708054bdf5981 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 14:25:53 -0000 --001a114ac54edcf708054bdf5981 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Apparently you can't attach a .pptx. The attachment is here (pptx and pdf): http://www.1-4-5.net/~dmm/ml/misc/musings.pptx http://www.1-4-5.net/~dmm/ml/misc/musings.pdf Thx, Dave On Wed, Mar 29, 2017 at 7:17 AM, David Meyer wrote: > > Hey Min-Suk, > > Totally agree we need to learn from our environment, and RL is a natural > approach. After all, the network is always changing, has adversaries, etc= . > All of this means. among other things, that we can't make simplifying > assumptions like stationary distributions, iid data, .... So RL is one w= ay > to attack these problems, and the classic algorithms you mention below ar= e > certainly a reasonable approach (I've been working with policy gradients > [0], trying to model/adapt the two-player game approach of AlphaGo to > networking; the problem there is that we don't have a source of labeled > expert data like the KGS Go server (https://www.gokgs.com/) to build the > supervised policy network....). > > You might also want to check out the recent "boot" of evolution strategie= s > as a black-box approach to RL (in particular no gradients). See [1], [2]= , > [3]. There is also a ton of code around if you want to try some of this > out (see e.g.,https://github.com/dennybritz/reinforcement-learning; this > one is in tensorflow). Finally, I've attached a few summary slides with > some of my musings on this topic from past talks. > > Thanks, > > Dave > > [BTW, two player minimax games seem to be popping up everywhere: AlphaGo, > variational autoencoders [4], GANs [5], and many others; something to thi= ng > about for our domain] > > [0] https://papers.nips.cc/paper/1713-policy-gradient- > methods-for-reinforcement-learning-with-function-approximation.pdf > [1] https://blog.openai.com/evolution-strategies/ > [2] https://arxiv.org/pdf/1703.03864.pdf > [3] http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pdf > [4] http://www.1-4-5.net/~dmm/ml/vae.pdf > [5] https://arxiv.org/pdf/1406.2661.pdf > > On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: > >> Hi Brian, >> >> >> As you mentioned by the prior email, anticipating network DDos >> attacks is really trendy issue to solve by ML techniques. >> >> We also make some efforts how to avoid fagile nodes by a trustworthy >> communication, that means quantifying trustworthiness of node with >> normalization of various requirements such as security function, bandwid= th >> and etc. >> >> We are freshly approaching in routing layer with confidence using our ow= n >> requirements, TPD(Trust Policy Distribution) and TD(Trust Degree). These >> requirements are considered to be solved by Reinforcement Learning >> (RL) that is one of the ML algorithms. RL is useful to control some of >> network policy about specific actions and states with reinforced and >> purnished rewards (+/-), but the problem is too slow to acquire satisifi= ed >> performance. Other ways to say it, anormaly dectection and regression >> analysis might be both efficient approaching methods to solve the issues >> Dave mentioned. >> >> >> Best Regards, >> >> >> Min-Suk Kim >> >> Senior Researcher / Ph.D. >> Intelligent IoE Network Research Section, >> *E*lectronics and *T*elecommunications *R*esearch *I*nstitute (*ETRI)* >> e-mail : mskim16@etri.re.kr >> http://www.etri.re.kr/ >> >> >> >> >> >> >> --001a114ac54edcf708054bdf5981 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Apparently you can't attach a .pptx. The attachment is= here (pptx and pdf):

http://www.1-4-5.net/~dmm/ml/misc/musings.pptx
<= a href=3D"http://www.1-4-5.net/~dmm/ml/misc/musings.pdf">http://www.1-4-5.n= et/~dmm/ml/misc/musings.pdf

Th= x,

Dave


On Wed, Mar 29, 2017 at 7:17 AM, = David Meyer <dmm@1-4-5.net> wrote:

Hey Min-Suk,

Tota= lly agree we need to learn from our environment, and RL is a natural approa= ch. After all, the network is always changing, has adversaries, etc. All of= this means. among other things, =C2=A0that we can't make simplifying a= ssumptions like stationary distributions, =C2=A0iid data, .... So RL is one= way to attack these problems, and the classic algorithms you mention below= are certainly a reasonable approach (I've been working with policy gra= dients [0], trying to model/adapt the two-player game approach of AlphaGo t= o networking; the problem there is that we don't have a source of label= ed expert data like the KGS Go server (https://www.gokgs.com/) to build the supervised policy= network....).=C2=A0

You might also want to check = out the recent "boot" of evolution strategies as a black-box appr= oach to RL (in particular no gradients). See [1], =C2=A0[2], =C2=A0[3]. The= re is also a ton of code around if you want to try some of this out (see e.= g.,https://github.com/dennybritz/reinforcement-learning<= /a>; this one is in tensorflow). Finally, I've attached a few summary s= lides with some of my musings on this topic from past talks.



[5]= =C2=A0htt= ps://arxiv.org/pdf/1406.2661.pdf

On Tue, Mar 28, 201= 7 at 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D <mskim16@etri.re.kr> wrote:

Hi Brian,


As you mentioned by the prior= email, anticipating network DDos attacks=C2=A0is=C2=A0really trendy issue = to solve by ML techniques.

We also make some efforts how= to=C2=A0avoid fagile nodes by=C2=A0a trustworthy communication, that means= quantifying trustworthiness of node=C2=A0with normalization=C2=A0of variou= s requirements such as security function,=C2=A0bandwidth and etc.

We are freshly approaching in= =C2=A0routing layer with confidence using our own requirements, TPD(Trust P= olicy Distribution) and TD(Trust Degree). These requirements are considered= to be solved by Reinforcement Learning (RL)=C2=A0that is one of the ML algorithms. RL is useful to control some o= f network=C2=A0policy about specific actions and states with reinforced and= purnished rewards (+/-), but the problem is too slow to=C2=A0acquire satis= ified performance. Other ways to=C2=A0say it,=C2=A0anormaly dectection and regression analysis=C2=A0might be=C2=A0both efficient=C2=A0= approaching methods to solve the issues Dave mentioned.


Best Regards,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Researcher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
E<= /strong>lectronics and=C2=A0Telecommunications=C2=A0Re= search=C2=A0Institute (ETRI)
e-mail =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0: =C2=A0mskim1= 6@etri.re.kr

=C2=A0

=C2=A0



--001a114ac54edcf708054bdf5981-- From nobody Wed Mar 29 07:33:45 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2CD24129532 for ; Wed, 29 Mar 2017 07:33:44 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id WijIKR_rvVOd for ; Wed, 29 Mar 2017 07:33:41 -0700 (PDT) Received: from mail-it0-x243.google.com (mail-it0-x243.google.com [IPv6:2607:f8b0:4001:c0b::243]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 7A9151296CE for ; Wed, 29 Mar 2017 07:33:22 -0700 (PDT) Received: by mail-it0-x243.google.com with SMTP id y18so10087821itc.2 for ; Wed, 29 Mar 2017 07:33:22 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:subject:from:in-reply-to:date:cc :content-transfer-encoding:message-id:references:to; bh=7zxDuIwiIKKtuWwHJ6TXDXX2UmePg2AaWxxyKxuMAjo=; b=R5PvtTqhPva5Dag/H3uq5VcwEBIKiUiYGkbIIQdg0iuriujHKxqVVzBQ2yOFj81WHF xt2l2ezt4HGghTD67fa49Uu65SV36yyR87FJYKTFzkAEgm5z4F4goRLG2WcLX4n3F9YO OytnEm1Dw67Sd7VDp9HVbuwPk9EfPhR9u+lr8byyD0ybKitA60pX3yRwJxY68WBEWAvS YHjw2uNNkL4T0B1unPQBk3vF66W4RTbpTpwUHElfY7wKNgGERuQqpOzatc+Y/JvWi3gt 8b25Y/YpIc2gk0PQwE4mPx4bCYnvB4Rm1eKUcbJx+UwtxM4O3qBLuxfSJcNFSaIneCzL 1R6g== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:subject:from:in-reply-to:date:cc :content-transfer-encoding:message-id:references:to; bh=7zxDuIwiIKKtuWwHJ6TXDXX2UmePg2AaWxxyKxuMAjo=; b=V04jTUyJm31lVAd8kj0nIGMgMyr7LfBCtaCAbZlPE3u02fViKW5bNCHCK3FYLOmJED iMuuOFKRDyEBo3Jo5rD4gHO6fkP2+U42UKfUKqV3I9sXAXaDvmNy2we8A8LsQjtoBByK THNeZFbInhXkHZVSvlCaCnR+pa54nSCop62BqRuicSaRgo1z7i8kNXpW/SmnzhcMmBxN XR0EEDcvWUw2zQPt5G1cwo/hyNffGk/8FbnPEvs4aMQ5Ryxz+4in4MFzKuW2d5BdQBNo LvWMlT/vrDkycKD66OhMvEsiGAuOKlSEPRd1RaZRYap2qSUaJeO96o01YfOkMIauFIEz GOyw== X-Gm-Message-State: AFeK/H3SLGbqHuJsjEWVHfmXNZFWUOaqBY6lyUiyBSqs4PhoEGmgAdxhzy2Otmm/FE0IPg== X-Received: by 10.36.92.20 with SMTP id q20mr1050158itb.49.1490798001909; Wed, 29 Mar 2017 07:33:21 -0700 (PDT) Received: from t2001067c037001284cb85a932fb00e8b.v6.meeting.ietf.org (t2001067c037001284cb85a932fb00e8b.v6.meeting.ietf.org. [2001:67c:370:128:4cb8:5a93:2fb0:e8b]) by smtp.gmail.com with ESMTPSA id e20sm4142645itc.3.2017.03.29.07.33.20 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Wed, 29 Mar 2017 07:33:20 -0700 (PDT) Content-Type: text/plain; charset=us-ascii Mime-Version: 1.0 (Mac OS X Mail 10.2 \(3259\)) From: Alex Galis In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> Date: Wed, 29 Mar 2017 09:33:19 -0500 Cc: "idnet@ietf.org" Content-Transfer-Encoding: quoted-printable Message-Id: <6044ED70-AED2-4219-B772-CA6EE56D780D@gmail.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> To: Sheng Jiang X-Mailer: Apple Mail (2.3259) Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests--> IDET meeting time and venue for Thursday? X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 14:33:44 -0000 Dear Sheng Would you be so kind as to provide the IDET meeting time and venue for = Thursday Thank you in advance Best Regards Alex Galis > On 28 Mar 2017, at 11:44, Sheng Jiang wrote: >=20 > Oops... An important typo. Try again: >=20 > we may have an informal meeting to discuss some common interests and = potential future activities (not "only" activities in IETF, but also = other STO or experimental trails, etc.) on Thursday morning. >=20 > Sheng > ________________________________________ > From: IDNET [idnet-bounces@ietf.org] on behalf of Sheng Jiang = [jiangsheng@huawei.com] > Sent: 29 March 2017 0:29 > To: idnet@ietf.org > Subject: [Idnet] Intelligence-Defined Network Architecture and Call = for Interests >=20 > Hi, all, >=20 > Although there are many understanding for Intelligence-Defined = Network, we are actually using this IDN as a term reference to the = SDN-beyond architecture that we presented in IETF97, see the below link. = A reference model is presented in page 3, while potential = standardization works is presented in page 9. >=20 > = https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-de= fined-network-01.pdf >=20 > Although it might be a little bit too early for AI/ML in network = giving the recent story of the concluded proposed NMLRG, we still would = like to call for interests in IDN. Anybody (on site in Chicago this = week) are interested in this or even wider topics regarding to AI/ML in = network, please contact me on jiangsheng@huawei.com . Then we may have = an informal meeting to discuss some common interests and potential = future activities (not any activities in IETF, but also other STO or = experimental trails, etc.) on Thursday morning. >=20 > FYI, we have already working on a Work Item, called IDN in the ETSI = NGP (Next Generation Protocol) ISG, links below. >=20 > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > = https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51= 011 >=20 > Meanwhile, please do use this mail list as a forum to discuss any = topics that may applying AI/ML into network area. >=20 > Best regards, >=20 > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet From nobody Wed Mar 29 07:34:01 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 34374129521 for ; Wed, 29 Mar 2017 07:34:00 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.698 X-Spam-Level: X-Spam-Status: No, score=-2.698 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id wAOWSjiXRghM for ; Wed, 29 Mar 2017 07:33:52 -0700 (PDT) Received: from mail-qk0-x22d.google.com (mail-qk0-x22d.google.com [IPv6:2607:f8b0:400d:c09::22d]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 2D9B2129541 for ; Wed, 29 Mar 2017 07:33:38 -0700 (PDT) Received: by mail-qk0-x22d.google.com with SMTP id f11so14367557qkb.0 for ; Wed, 29 Mar 2017 07:33:38 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=MdRXJhzmAazzrqrKJJpvHODES9D390Gf7p5YwD8Bbgg=; b=kGPXUWtvnW+F6WF3ZoDMwNlvSoLNFOBQSmmy4h9Q+aNFa3Sr3LZCOG3+e2qeghFU7a vAWTt5ATATbBqJ1MDKpWk9LRa3Bn7E/gP/g4vuAJWDU9ZQMOswM75x03qbl26cGcXKJI L7rrllyF/9TB5QpfIOpoipmn9buQwBBkfFanERmnZb9I96n8vMicTHPmuQGP/aCXLKaU GQkM8ygpmqbWPYYaz7mfYYIlsQl274YvrYsgkLoD1VERlaaq0yjpUbW+jzm/SE+G+37I aE4SD4V0eUDBMnO2+iOQwyIyg1kpIka57kdqS7n1+spKXiVhXdHPwC4h2XgCzWYJere0 blZA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=MdRXJhzmAazzrqrKJJpvHODES9D390Gf7p5YwD8Bbgg=; b=ZkcRjR4Qp/jqvKWUCqJqFP9ZsSuB4DgXUy86g9LSUy6LpV9+/OmqoNgWW4tx9GbWuY KLKh/A7T94heCtZ+bXWY9qjiOS9ToVewqKVETr4K0jCeT+wBDCHkrfkJ8GdENg93vorI 2lltuvYSYqHfT/WL89WhddgKN/Ini1o+CcKe/bSnQF4d5P0jKfPZgL9QlDcuTWUkW85G R1AUUkFjrfSmPoQJXzdFND79NS/HMRPb9nLxsXHvZh3c8FRpPe5qETCeRGilhLvOoD9q AhpcB/4xDcpHGc8Y88JP/TaHwPd+58DEv964z2XFUtQJLzPJuJrr0iqsGfdqcSBFTfg1 Zhyg== X-Gm-Message-State: AFeK/H0dKiUjWVMPKq3dWsrBRBa5oFiJ1cMbYhQhvlXN+rtsqwM569q1DmrTvUqDbV9kY+DtWzUMn25iqZsX7A== X-Received: by 10.55.75.146 with SMTP id y140mr342304qka.200.1490798017068; Wed, 29 Mar 2017 07:33:37 -0700 (PDT) MIME-Version: 1.0 Received: by 10.140.97.163 with HTTP; Wed, 29 Mar 2017 07:33:36 -0700 (PDT) In-Reply-To: <84ff06ac-61e7-3e71-f6f9-c78335c69aaf@inria.fr> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <20170328182530.GP4808@spectre> <84ff06ac-61e7-3e71-f6f9-c78335c69aaf@inria.fr> From: Chris Hammerschmidt Date: Wed, 29 Mar 2017 16:33:36 +0200 Message-ID: To: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a114819443d62a3054bdf76f4 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 14:34:00 -0000 --001a114819443d62a3054bdf76f4 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi, equally important to having datasets* is proposing benchmarks with the datasets.* Right now, everyone has different expectations and from my personal experience, many reviewers at networking conferences won't accept a "typical" machine learning paper with applications in networking consisting of data+model+evaluation. Expectations range from something like a large-scale practical application case to addressing worries about adversarial influence on the learned model, or systems that take not only care of machine-learning malicious/anomalous/interesting behavior but want to have guidelines how to apply it, e.g. filtering rules and parameter/threshold setting guidelines. Often, these values have to be set with domain knowledge, adjusted to the specific application case (as networks can be very different). The lack of benchmarks makes it incredibly hard to compare different solutions to each other, or even to just apply a new method to an old problem; and indeed, reviewers have pointed out to me that the problem of machine learning from network traffic has been solved already and there is no point in further papers on this topic. A *shared benchmark* solves this problem by delegating the argument for the relevance ONCE while setting up the benchmark, rather than distributing the task to each author. Cheers Christian On 28 March 2017 at 20:46, J=C3=A9r=C3=B4me Fran=C3=A7ois wrote: > Hi > > Le 28/03/2017 =C3=A0 20:25, Pedro Martinez-Julia a =C3=A9crit : > > On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote: > >> Hey Sheng, > >> > >> I just wanted to revive my key concern on [0] (same one I made at the > >> NMRL): The hard parts of getting Machine Learning intelligence into > >> Networking is the Machine Learning part. In addition, successful > deployment > >> of ML requires knowledge of ML combined with domain knowledge. We > >> definitely have the domain knowledge; the problem is that we don't hav= e > the > >> ML knowledge, and this is one of the big factors holding us back; see > e.g. > >> Andrew's discussion of talent in [1]. Slides such as [0] seem to impl= y > >> that *someone else* (in particular, not us) will handle the ML part o= f > all > >> of this. I'll just note that in general successful deployments of ML > don't > >> work this way; the domain experts will have to learn ML (and vice vers= a) > >> for us to be successful (again, see [1] and many others). > > Dear Dave, > > > > You are true in that ML/domain knowledge is necessary but, however it i= s > > worth to take into account that it is not strictly required and it will > > even be counterproductive in some (or maybe most) situations. At the en= d > > of the day, encouraging (or forcing) a network expert to learn ML is > > quite difficult, the results will be delayed until the learning phase > > ends, and (most probably) s/he will never get a better solution than a > > person that has been an expert in ML from a long time ago. Therefore, i= t > > is better to make separate experts (in ML and the domain itself) to > > collaborate in a common solution. Therefore, and I think it has been > > mentioned before, we have to (try to) enroll experts in ML to the IDNET > > group and see what can we do together... > This is a general trend that only a single person cannot be expert in > everything. Actually, a good network expert may require good ML but also > good software skills (including software formal verification knowledge). > So, in my opinion the problem is larger. > > Enhancing collaboration between network and ML expert is a path that > starts in many company and insitutes I think. Discussing with ML > experts, they are usually open and happy to discover new "use cases" > but their first question will be "do you have some labelled datasets > that we can work with" which relates to the problem raised in previous > emails about open datasets. > > In my opinion, if we wan to attract ML experts in our dicussions, we > should identify few scenrios, defined them precisely and provide an open > dataset. By defining them, I mean we have to give them all background > they need to understand (that can be built incrementally through > discussion) in a well-documented format. > > jerome > > >> Perhaps a useful exercise would be to write an ID that makes your > >> assumptions explicit? > >> > >> Thanks, > >> Dave > > Regards, > > Pedro > > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a114819443d62a3054bdf76f4 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hi,

equally important to having dataset= s is proposing benchmarks with the datasets. Right now, everyone has= different expectations and from my personal experience, many reviewers at = networking conferences won't accept a "typical" machine learn= ing paper with applications in networking consisting of data+model+evaluati= on. Expectations range from something like a large-scale practical applicat= ion case=C2=A0to addressing worries about adversarial influence on the lear= ned model, or systems that take=C2=A0not only care of machine-learning mali= cious/anomalous/interesting behavior but want to have guidelines how to app= ly it, e.g. filtering rules and parameter/threshold setting guidelines. Oft= en, these values have to be set with domain knowledge, adjusted to the spec= ific application case (as networks can be very different).

The lack of benchmarks makes it incredibly hard to compare differe= nt solutions to each other, or even to just apply a new method to an old pr= oblem; and indeed, reviewers have pointed out to me that the problem of mac= hine learning from network traffic has been solved already and there is no = point in further papers on this topic. A shared benchmark=C2=A0solve= s this problem by delegating the argument for the relevance ONCE while sett= ing up the benchmark, rather than distributing the task to each author.

Cheers
Christian

On 28 March 2017 at 20:46, J=C3= =A9r=C3=B4me Fran=C3=A7ois <jerome.francois@inria.fr>= wrote:
Hi

Le 28/03/2017 =C3=A0 20:25, Pedro Martinez-Julia a =C3=A9crit :
> On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote:
>> Hey Sheng,
>>
>> I just wanted to revive my key concern on [0] (same one I made at = the
>> NMRL): The hard parts of getting Machine Learning intelligence int= o
>> Networking is the Machine Learning part. In addition, successful d= eployment
>> of ML requires knowledge of ML combined with domain knowledge. We<= br> >> definitely have the domain knowledge; the problem is that we don&#= 39;t have the
>> ML knowledge, and this is one of the big factors holding us back; = see e.g.
>> Andrew's discussion of talent in [1].=C2=A0 Slides such as [0]= seem to imply
>> that *someone else* (in particular, not us)=C2=A0 will handle the = ML part of all
>> of this. I'll just note that in general successful deployments= of ML don't
>> work this way; the domain experts will have to learn ML (and vice = versa)
>> for us to be successful (again, see [1] and many others).
> Dear Dave,
>
> You are true in that ML/domain knowledge is necessary but, however it = is
> worth to take into account that it is not strictly required and it wil= l
> even be counterproductive in some (or maybe most) situations. At the e= nd
> of the day, encouraging (or forcing) a network expert to learn ML is > quite difficult, the results will be delayed until the learning phase<= br> > ends, and (most probably) s/he will never get a better solution than a=
> person that has been an expert in ML from a long time ago. Therefore, = it
> is better to make separate experts (in ML and the domain itself) to > collaborate in a common solution. Therefore, and I think it has been > mentioned before, we have to (try to) enroll experts in ML to the IDNE= T
> group and see what can we do together...
This is a general trend that only a single person cannot be exp= ert in
everything. Actually, a good network expert may require good ML but also good software skills (including software formal verification knowledge). So, in my opinion the problem is larger.

Enhancing collaboration between network and ML expert is a path that
starts in many company and insitutes I think. Discussing with ML
experts, they are usually open and happy to discover new "use cases&qu= ot;
but their first question will be "do you have some labelled datasets that we can work with" which relates to the problem raised in previous=
emails about open datasets.

In my opinion, if we wan to attract ML experts in our dicussions, we
should identify few scenrios, defined them precisely and provide an open dataset. By defining them, I mean we have to give them all background
they need to understand (that can be built incrementally through
discussion) in a well-documented format.

jerome

>> Perhaps a useful exercise would be to write an ID that makes your<= br> >> assumptions explicit?
>>
>> Thanks,
>> Dave
> Regards,
> Pedro
>


____________________________= ___________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--001a114819443d62a3054bdf76f4-- From nobody Wed Mar 29 07:58:14 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 4ED401287A5 for ; Wed, 29 Mar 2017 07:58:12 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.898 X-Spam-Level: X-Spam-Status: No, score=-1.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 6BWbqXYGs0cK for ; Wed, 29 Mar 2017 07:58:09 -0700 (PDT) Received: from mail-qt0-x234.google.com (mail-qt0-x234.google.com [IPv6:2607:f8b0:400d:c0d::234]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 8D130129552 for ; Wed, 29 Mar 2017 07:58:02 -0700 (PDT) Received: by mail-qt0-x234.google.com with SMTP id x35so14758905qtc.2 for ; Wed, 29 Mar 2017 07:58:02 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=P6XCFz6Lp60p6am/Xii3mdVZ/Hluh6JESHu1zBnoaak=; b=bs15Uif9AuyD5RLUGBBauIbzkPyTDCdiRCNCMGXXd6X4iNkwtp7ybDwNB36jzcLouA fTg3nX45ZM1YsdENDtOfXaJ57Oz9yVZR1LgqFBzZFUZ0U0pAycvPWGTIpYe5Kl92Yz2a uGrCCeiMNged0OdVLhSHiuZtflqySQOxst1NDZC1WffV73ozZIm0L6ixjBZX/9EAwe+u egv3cr1n0K/JdMEdRvNSADte/TklmDLYKgtF3mBdSQtfSVD+D//9JU79mXdFY9f1WEeC faEepc11isaNJaAHaEiUmBVtpfLSR4WoKNQfuVdHE+cTWbHzdGpLTkidY+aghrt4dIIX shqQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=P6XCFz6Lp60p6am/Xii3mdVZ/Hluh6JESHu1zBnoaak=; b=jsRFV58S3aSKLR7GPIw/zrDeGMUw5+5TqOQXaJU1+JZJEKZD5OkaGRVWImvcJD3z7h ZJDJCuN8eR6+Tqr3IZ5Yf5GXAZf1KKDSeuTWJ2A2m/jbwaKz4nAZX/d6t7Z8r9RAsNBs zcLrAcJL6z5k+aQXDREcQeSoirv+Bnr5jJNw4n88claXIThYumxMcnnKKU/SOSyi5Zic 6e/DR1oBoW01//TrdUyuu4dqsMkqzrdZdMBTC8Ybx4xTvgqvNtZz+KoTgQ4keQZDkzXz x/i5x+02piVCwMlyKe8uujoVOBhUtETMpOEOovFmL/9mzES61rtfviQh+gWZDMCZ3KCP kFDw== X-Gm-Message-State: AFeK/H1+9zaILWGC7Ehrq8W1vqXVAj1U6DSQfeuQ0SBu7v7EsWgF2TYF4gXbkTcwJ6Vm204u4DKpWmWVKNGCow== X-Received: by 10.237.38.229 with SMTP id q92mr1033296qtd.6.1490799481182; Wed, 29 Mar 2017 07:58:01 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Wed, 29 Mar 2017 07:58:00 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <20170328182530.GP4808@spectre> <84ff06ac-61e7-3e71-f6f9-c78335c69aaf@inria.fr> From: David Meyer Date: Wed, 29 Mar 2017 07:58:00 -0700 Message-ID: To: Chris Hammerschmidt Cc: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , list-idnet Content-Type: multipart/alternative; boundary=94eb2c0bb94e826250054bdfcded Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 14:58:12 -0000 --94eb2c0bb94e826250054bdfcded Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Chris, Totally agree. This is part of the reason we need standardized data sets, namely, so we can compare results and understand if we're making progress. See e.g., http://image-net.org/challenges/LSVRC/2016/ for an example. The result has been the steady ratcheting down of error rates to super-human levels. We have nothing like this for networking, and as I have been saying, that is one of our biggest challenges. Dave On Wed, Mar 29, 2017 at 7:33 AM, Chris Hammerschmidt wrote: > Hi, > > equally important to having datasets* is proposing benchmarks with the > datasets.* Right now, everyone has different expectations and from my > personal experience, many reviewers at networking conferences won't accep= t > a "typical" machine learning paper with applications in networking > consisting of data+model+evaluation. Expectations range from something li= ke > a large-scale practical application case to addressing worries about > adversarial influence on the learned model, or systems that take not only > care of machine-learning malicious/anomalous/interesting behavior but > want to have guidelines how to apply it, e.g. filtering rules and > parameter/threshold setting guidelines. Often, these values have to be se= t > with domain knowledge, adjusted to the specific application case (as > networks can be very different). > > The lack of benchmarks makes it incredibly hard to compare different > solutions to each other, or even to just apply a new method to an old > problem; and indeed, reviewers have pointed out to me that the problem of > machine learning from network traffic has been solved already and there i= s > no point in further papers on this topic. A *shared benchmark* solves > this problem by delegating the argument for the relevance ONCE while > setting up the benchmark, rather than distributing the task to each autho= r. > > Cheers > Christian > > On 28 March 2017 at 20:46, J=C3=A9r=C3=B4me Fran=C3=A7ois > wrote: > >> Hi >> >> Le 28/03/2017 =C3=A0 20:25, Pedro Martinez-Julia a =C3=A9crit : >> > On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote: >> >> Hey Sheng, >> >> >> >> I just wanted to revive my key concern on [0] (same one I made at the >> >> NMRL): The hard parts of getting Machine Learning intelligence into >> >> Networking is the Machine Learning part. In addition, successful >> deployment >> >> of ML requires knowledge of ML combined with domain knowledge. We >> >> definitely have the domain knowledge; the problem is that we don't >> have the >> >> ML knowledge, and this is one of the big factors holding us back; see >> e.g. >> >> Andrew's discussion of talent in [1]. Slides such as [0] seem to imp= ly >> >> that *someone else* (in particular, not us) will handle the ML part >> of all >> >> of this. I'll just note that in general successful deployments of ML >> don't >> >> work this way; the domain experts will have to learn ML (and vice >> versa) >> >> for us to be successful (again, see [1] and many others). >> > Dear Dave, >> > >> > You are true in that ML/domain knowledge is necessary but, however it = is >> > worth to take into account that it is not strictly required and it wil= l >> > even be counterproductive in some (or maybe most) situations. At the e= nd >> > of the day, encouraging (or forcing) a network expert to learn ML is >> > quite difficult, the results will be delayed until the learning phase >> > ends, and (most probably) s/he will never get a better solution than a >> > person that has been an expert in ML from a long time ago. Therefore, = it >> > is better to make separate experts (in ML and the domain itself) to >> > collaborate in a common solution. Therefore, and I think it has been >> > mentioned before, we have to (try to) enroll experts in ML to the IDNE= T >> > group and see what can we do together... >> This is a general trend that only a single person cannot be expert in >> everything. Actually, a good network expert may require good ML but also >> good software skills (including software formal verification knowledge). >> So, in my opinion the problem is larger. >> >> Enhancing collaboration between network and ML expert is a path that >> starts in many company and insitutes I think. Discussing with ML >> experts, they are usually open and happy to discover new "use cases" >> but their first question will be "do you have some labelled datasets >> that we can work with" which relates to the problem raised in previous >> emails about open datasets. >> >> In my opinion, if we wan to attract ML experts in our dicussions, we >> should identify few scenrios, defined them precisely and provide an open >> dataset. By defining them, I mean we have to give them all background >> they need to understand (that can be built incrementally through >> discussion) in a well-documented format. >> >> jerome >> >> >> Perhaps a useful exercise would be to write an ID that makes your >> >> assumptions explicit? >> >> >> >> Thanks, >> >> Dave >> > Regards, >> > Pedro >> > >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --94eb2c0bb94e826250054bdfcded Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Chris,

Totally agree. This is part of t= he reason we need standardized data sets, namely, so we can compare results= and understand if we're making progress. See e.g.,=C2=A0http://image-net.org/challenges/L= SVRC/2016/ for an example.=C2=A0 The result has been the steady=C2=A0ra= tcheting down of error rates to super-human levels. We have nothing like th= is for networking, and as I have been saying, that is one of our biggest ch= allenges.

Dave


On Wed, Mar 29, 2017 at 7:= 33 AM, Chris Hammerschmidt <laxris@gmail.com> wrote:
Hi,

equally i= mportant to having datasets is proposing benchmarks with the datasets. Right now, everyone has different expectations and from my personal expe= rience, many reviewers at networking conferences won't accept a "t= ypical" machine learning paper with applications in networking consist= ing of data+model+evaluation. Expectations range from something like a larg= e-scale practical application case=C2=A0to addressing worries about adversa= rial influence on the learned model, or systems that take=C2=A0not only car= e of machine-learning malicious/anomalous/interesting behavior but wan= t to have guidelines how to apply it, e.g. filtering rules and parameter/th= reshold setting guidelines. Often, these values have to be set with domain = knowledge, adjusted to the specific application case (as networks can be ve= ry different).

The lack of benchmarks makes it inc= redibly hard to compare different solutions to each other, or even to just = apply a new method to an old problem; and indeed, reviewers have pointed ou= t to me that the problem of machine learning from network traffic has been = solved already and there is no point in further papers on this topic. A = shared benchmark=C2=A0solves this problem by delegating the argument fo= r the relevance ONCE while setting up the benchmark, rather than distributi= ng the task to each author.

Cheers
Chris= tian

On 28 March 2017 at 20:46, J=C3=A9r= =C3=B4me Fran=C3=A7ois <jerome.francois@inria.fr> wro= te:
Hi

Le 28/03/2017 =C3=A0 20:25, Pedro Martinez-Julia a =C3=A9crit :
> On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote:
>> Hey Sheng,
>>
>> I just wanted to revive my key concern on [0] (same one I made at = the
>> NMRL): The hard parts of getting Machine Learning intelligence int= o
>> Networking is the Machine Learning part. In addition, successful d= eployment
>> of ML requires knowledge of ML combined with domain knowledge. We<= br> >> definitely have the domain knowledge; the problem is that we don&#= 39;t have the
>> ML knowledge, and this is one of the big factors holding us back; = see e.g.
>> Andrew's discussion of talent in [1].=C2=A0 Slides such as [0]= seem to imply
>> that *someone else* (in particular, not us)=C2=A0 will handle the = ML part of all
>> of this. I'll just note that in general successful deployments= of ML don't
>> work this way; the domain experts will have to learn ML (and vice = versa)
>> for us to be successful (again, see [1] and many others).
> Dear Dave,
>
> You are true in that ML/domain knowledge is necessary but, however it = is
> worth to take into account that it is not strictly required and it wil= l
> even be counterproductive in some (or maybe most) situations. At the e= nd
> of the day, encouraging (or forcing) a network expert to learn ML is > quite difficult, the results will be delayed until the learning phase<= br> > ends, and (most probably) s/he will never get a better solution than a=
> person that has been an expert in ML from a long time ago. Therefore, = it
> is better to make separate experts (in ML and the domain itself) to > collaborate in a common solution. Therefore, and I think it has been > mentioned before, we have to (try to) enroll experts in ML to the IDNE= T
> group and see what can we do together...
This is a general trend that only a single person cannot be exp= ert in
everything. Actually, a good network expert may require good ML but also good software skills (including software formal verification knowledge). So, in my opinion the problem is larger.

Enhancing collaboration between network and ML expert is a path that
starts in many company and insitutes I think. Discussing with ML
experts, they are usually open and happy to discover new "use cases&qu= ot;
but their first question will be "do you have some labelled datasets that we can work with" which relates to the problem raised in previous=
emails about open datasets.

In my opinion, if we wan to attract ML experts in our dicussions, we
should identify few scenrios, defined them precisely and provide an open dataset. By defining them, I mean we have to give them all background
they need to understand (that can be built incrementally through
discussion) in a well-documented format.

jerome

>> Perhaps a useful exercise would be to write an ID that makes your<= br> >> assumptions explicit?
>>
>> Thanks,
>> Dave
> Regards,
> Pedro
>


_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


--94eb2c0bb94e826250054bdfcded-- From nobody Wed Mar 29 08:01:29 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 722CF1294C7 for ; Wed, 29 Mar 2017 08:01:28 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.201 X-Spam-Level: X-Spam-Status: No, score=-4.201 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id K62yLnAwfITM for ; Wed, 29 Mar 2017 08:01:25 -0700 (PDT) Received: from ns1.nict.go.jp (ns1.nict.go.jp [IPv6:2001:df0:232:300::1]) by ietfa.amsl.com (Postfix) with ESMTP id 186BC126DD9 for ; Wed, 29 Mar 2017 08:01:24 -0700 (PDT) Received: from gw1.nict.go.jp (gw1.nict.go.jp [133.243.18.250]) by ns1.nict.go.jp with ESMTP id v2TF1NXf021231 for ; Thu, 30 Mar 2017 00:01:24 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw1.nict.go.jp with ESMTP id v2TF1NRP021166 for ; Thu, 30 Mar 2017 00:01:23 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id 5A80CAA59 for ; Thu, 30 Mar 2017 00:01:23 +0900 (JST) Date: Thu, 30 Mar 2017 00:01:17 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170329150116.GW4808@spectre> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <20170328182530.GP4808@spectre> <84ff06ac-61e7-3e71-f6f9-c78335c69aaf@inria.fr> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith1 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 15:01:28 -0000 Hi, In the long term, having some good-quality and widely-accepted datasets is essential for the demonstration of qualities (benchmarking) as well as continuous assessment (Is my solution doing right? How good is it?). That said, I think that building and provisioning such datasets is a key role that can only be played by operators, since they have tons of data from which obtaining them. The only issues I see are the anonymization of such data, to protect both the privacy of users and the potentially confidential information from the operator itself, and the selection of the *good quality* extracts. Therefore, I hereby call to anyone from the list that is part of (or involved with) an operator to please try to work with us on this issue and evaluate the possible alternatives to get/build such datasets. Thank you very much. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Wed Mar 29 08:29:44 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 03877129526 for ; Wed, 29 Mar 2017 08:29:43 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id ZNOHJ9DPMgxO for ; Wed, 29 Mar 2017 08:29:39 -0700 (PDT) Received: from smtpeg.etri.re.kr (smtpeg2.etri.re.kr [129.254.27.142]) (using TLSv1 with cipher AES128-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 6CCAB129613 for ; Wed, 29 Mar 2017 08:29:38 -0700 (PDT) Received: from SMTP4.etri.info (129.254.28.74) by SMTPEG2.etri.info (129.254.27.142) with Microsoft SMTP Server (TLS) id 14.3.319.2; Thu, 30 Mar 2017 00:29:36 +0900 Received: from SMTP1.etri.info ([169.254.1.111]) by SMTP4.etri.info ([10.2.6.33]) with mapi id 14.03.0319.002; Thu, 30 Mar 2017 00:29:31 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: David Meyer CC: Brian Njenga , =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , "Oscar Mauricio Caicedo Rendon" , Sheng Jiang , "idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGp43QAgAAR6oCAAAWvAIAAA0cAgADS4nyAAHZ5AIAAAmIAgACdXno= Date: Wed, 29 Mar 2017 15:29:30 +0000 Message-ID: <5BC916BD50F92F45870ABA46212CB29CE4D07A@SMTP1.etri.info> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> , In-Reply-To: Accept-Language: ko-KR, en-US Content-Language: ko-KR X-MS-Has-Attach: yes X-MS-TNEF-Correlator: x-originating-ip: [129.254.28.42] Content-Type: multipart/mixed; boundary="_004_5BC916BD50F92F45870ABA46212CB29CE4D07ASMTP1etriinfo_" MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 15:29:43 -0000 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(2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 05doPTOThULd for ; Wed, 29 Mar 2017 10:12:08 -0700 (PDT) Received: from mail-qk0-x231.google.com (mail-qk0-x231.google.com [IPv6:2607:f8b0:400d:c09::231]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 90AB1128D8B for ; Wed, 29 Mar 2017 10:12:08 -0700 (PDT) Received: by mail-qk0-x231.google.com with SMTP id d10so18847391qke.1 for ; Wed, 29 Mar 2017 10:12:08 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=/jrjMnk1x9n9gcE6OS/jua2zAokcla8iXX0stCT3eSA=; b=riKH+dHrxKgp+T8CFHtNtLJuq4eg7Akaq++cJeKog5wLd4qIA4P27ESHy/ONIFJyMS 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10.55.177.68 with SMTP id a65mr1603593qkf.45.1490807527522; Wed, 29 Mar 2017 10:12:07 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Wed, 29 Mar 2017 10:12:06 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <5BC916BD50F92F45870ABA46212CB29CE4D07A@SMTP1.etri.info> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> <5BC916BD50F92F45870ABA46212CB29CE4D07A@SMTP1.etri.info> From: David Meyer Date: Wed, 29 Mar 2017 10:12:06 -0700 Message-ID: To: =?UTF-8?B?6rmA66+87ISd?= Cc: Brian Njenga , =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c070e981b71c6054be1ad6a Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 17:12:13 -0000 --94eb2c070e981b71c6054be1ad6a Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Min-Suk, On Wed, Mar 29, 2017 at 8:29 AM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: > Hi Dave, > > > Thank you for giving me the great information. > > I absolutely agree your opinion that we need real ML data applied by data > pre-processing so that we have been already trying to make available ML > data on many ways such as clustering and classification. (using datasets = of > contents and URL) > > It's so challenge-able steps before using adaptive ML algorithm to networ= k > field. > > As you mentioned, RL is classical ML algorithm, but it is rapidly going > develpment and make great results with tensorflow in many fields, > unfortunately not network. > > For our tutorial, I attach some of practical examples with tensorflow as > below, > > https://github.com/tensorflow/models > > > Additionally, I submitted a personal draft to NMLRG even if it was closed > from last meeting. It's about collaborative distributed multi-agent using > re-inforcement learning and we trying to apply it to network real > architecture. > > The attachment is on the email. I really appreciate giving me a small > piece of your feedback and comment if you have a chance. > Thanks. I will try to read/comment later today. Thanks again, Dave > > Sincerely, > > > Min-Suk Kim > > Senior Researcher / Ph.D. > Intelligent IoE Network Research Section, > ETRI > > > > > > > ------------------------------ > *=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C : *"David Meyer" > *=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C : *2017-03-29 23:25:48 ( +09:00 ) > *=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C : *=EA=B9=80=EB=AF=BC=EC=84=9D > *=EC=B0=B8=EC=A1=B0 : *Brian Njenga , J=C3=A9r= =C3=B4me Fran=C3=A7ois < > jerome.francois@inria.fr>, Oscar Mauricio Caicedo Rendon < > omcaicedo@unicauca.edu.co>, Sheng Jiang , > idnet@ietf.org > *=EC=A0=9C=EB=AA=A9 : *Re: [Idnet] Intelligence-Defined Network Architect= ure and Call for > Interests > > > Apparently you can't attach a .pptx. The attachment is here (pptx and > pdf): > > > http://www.1-4-5.net/~dmm/ml/misc/musings.pptx > http://www.1-4-5.net/~dmm/ml/misc/musings.pdf > > > > > Thx, > > > Dave > > > > > On Wed, Mar 29, 2017 at 7:17 AM, David Meyer wrote: > > > >> >> Hey Min-Suk, >> >> >> Totally agree we need to learn from our environment, and RL is a natural >> approach. After all, the network is always changing, has adversaries, et= c. >> All of this means. among other things, that we can't make simplifying >> assumptions like stationary distributions, iid data, .... So RL is one = way >> to attack these problems, and the classic algorithms you mention below a= re >> certainly a reasonable approach (I've been working with policy gradients >> [0], trying to model/adapt the two-player game approach of AlphaGo to >> networking; the problem there is that we don't have a source of labeled >> expert data like the KGS Go server (https://www.gokgs.com/) to build the >> supervised policy network....). >> >> >> You might also want to check out the recent "boot" of evolution >> strategies as a black-box approach to RL (in particular no gradients). S= ee >> [1], [2], [3]. There is also a ton of code around if you want to try s= ome >> of this out (see e.g.,https://github.com/dennyb >> ritz/reinforcement-learning; this one is in tensorflow). Finally, I've >> attached a few summary slides with some of my musings on this topic from >> past talks. >> >> >> Thanks, >> >> >> Dave >> >> >> [BTW, two player minimax games seem to be popping up everywhere: AlphaGo= , >> variational autoencoders [4], GANs [5], and many others; something to th= ing >> about for our domain] >> >> >> [0] https://papers.nips.cc/paper/1713-policy-gradient-method >> s-for-reinforcement-learning-with-function-approximation.pdf >> [1] https://blog.openai.com/evolution-strategies/ >> [2] https://arxiv.org/pdf/1703.03864.pdf >> [3] http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wierstra14a.pd= f >> [4] http://www.1-4-5.net/~dmm/ml/vae.pdf >> [5] https://arxiv.org/pdf/1406.2661.pdf >> >> >> >> >> On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: >> >> >> >>> Hi Brian, >>> >>> >>> >>> As you mentioned by the prior email, anticipating network DDos >>> attacks is really trendy issue to solve by ML techniques. >>> >>> We also make some efforts how to avoid fagile nodes by a trustworthy >>> communication, that means quantifying trustworthiness of node with >>> normalization of various requirements such as security function, bandwi= dth >>> and etc. >>> >>> We are freshly approaching in routing layer with confidence using our >>> own requirements, TPD(Trust Policy Distribution) and TD(Trust Degree). >>> These requirements are considered to be solved by Reinforcement Learnin= g >>> (RL) that is one of the ML algorithms. RL is useful to control some of >>> network policy about specific actions and states with reinforced and >>> purnished rewards (+/-), but the problem is too slow to acquire satisif= ied >>> performance. Other ways to say it, anormaly dectection and regression >>> analysis might be both efficient approaching methods to solve the issue= s >>> Dave mentioned. >>> >>> >>> >>> Best Regards, >>> >>> >>> Min-Suk Kim >>> >>> Senior Researcher / Ph.D. >>> Intelligent IoE Network Research Section, >>> *E*lectronics and *T*elecommunications *R*esearch *I*nstitute (*ET**R* >>> *I)* >>> e-mail : mskim16@etri.re.kr >>> http://www.etri.re.kr/ >>> >>> >>> >>> >>> >>> >>> >>> > --94eb2c070e981b71c6054be1ad6a Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable --94eb2c070e981b71c6054be1ad6a-- From nobody Wed Mar 29 10:38:13 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 10CDB1279EB for ; Wed, 29 Mar 2017 10:38:11 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id P4a_Gpn4uxVT for ; Wed, 29 Mar 2017 10:38:07 -0700 (PDT) Received: from smtpeg.etri.re.kr (smtpeg2.etri.re.kr [129.254.27.142]) (using TLSv1 with cipher AES128-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D0655127698 for ; Wed, 29 Mar 2017 10:38:06 -0700 (PDT) Received: from SMTP3.etri.info (129.254.28.73) by SMTPEG2.etri.info (129.254.27.142) with Microsoft SMTP Server (TLS) id 14.3.319.2; Thu, 30 Mar 2017 02:38:04 +0900 Received: from SMTP1.etri.info ([169.254.1.111]) by SMTP3.etri.info ([10.2.6.32]) with mapi id 14.03.0319.002; Thu, 30 Mar 2017 02:37:59 +0900 From: =?utf-8?B?6rmA66+87ISd?= To: David Meyer CC: Brian Njenga , =?utf-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , "Oscar Mauricio Caicedo Rendon" , Sheng Jiang , "idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGp43QAgAAR6oCAAAWvAIAAA0cAgADS4nyAAHZ5AIAAAmIAgACdXnr//5EiAIAAnhz8 Date: Wed, 29 Mar 2017 17:37:59 +0000 Message-ID: <793B28A1-395C-4570-A971-885801A72FE0@etri.re.kr> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> <5BC916BD50F92F45870ABA46212CB29CE4D07A@SMTP1.etri.info>, In-Reply-To: Accept-Language: ko-KR, en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: Content-Type: multipart/alternative; boundary="_000_793B28A1395C4570A971885801A72FE0etrirekr_" MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: 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DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=vrl0HkyXmKRp9fc8RlMWLqKNK5HHgtLEroD0My1MpMM=; b=ckg0Hbc6ANGCKdKS2L118H5lyShKoBl9hfx8op/IlakOHDY6wcM+18HhDd6+OZGR20 S55AAVTJ+iwehyiM2BAx00kA9vWGuHd0nkHkLQmOklBgrQnUJpHrMiRNhslj4uo5yeMi kGFHyX2PwvHL3/ndPDDT3QGLuQZTQAJLX8GsrbjOGsViklu9qr+NEXNCfWK1F1+gUu+6 pxF1M1HoZR3lqgrEDyL6lcguuA6ouS9EjSkRmxwdwWkfPCH0QX2QMRBxmq/tkRzE09tb YoWp7p9i8nkIzo2cibbdGbdzFnWZoGolGApVgaeKI/wjeJHoCjY3QL4tDJ4Z6os+1Nd8 KESQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=vrl0HkyXmKRp9fc8RlMWLqKNK5HHgtLEroD0My1MpMM=; b=OSQb9C542UKd2nFSvbd/1nzxi0UPEFXdwgnHBX59sn1znVfw6toj75Au5lnsp6XE/9 yE6RXMcbWwy+Xp0LoyKMCFRGkxeBiJclMcHpTH21UeI+7NOtPPn8ehWELWKFZx9M7RYW I85b0o9el1bRaxgEaZCZR9ZbVUrTj/rEb457YUgGs9Crusjld+QETacRk+1d6DULs4aJ YBIvqVbpV1n7R0vAB896ZDubKMCvDL4+Nc6bqNeyldR7NFyj3QxY5cQc6HM4ROx9CcjZ UxQRVOVXu7Nvu1wCSqNBzsgcB5tMRC5rmLGo5NEtNkAYafXCpgqt1cpe94o/rMAToEOP WSwA== X-Gm-Message-State: AFeK/H0NCwGRT5+NGBhOaeIwJzslD0hKe7SPvQCiaKqry4x7nCe6i6Sn+E7RxF+qG2IrxlrN5I5c201IL1yKcw== X-Received: by 10.55.65.81 with SMTP id o78mr1848234qka.82.1490811514147; Wed, 29 Mar 2017 11:18:34 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Wed, 29 Mar 2017 11:18:33 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <793B28A1-395C-4570-A971-885801A72FE0@etri.re.kr> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5BC916BD50F92F45870ABA46212CB29CE4C966@SMTP1.etri.info> <5BC916BD50F92F45870ABA46212CB29CE4D07A@SMTP1.etri.info> <793B28A1-395C-4570-A971-885801A72FE0@etri.re.kr> From: David Meyer Date: Wed, 29 Mar 2017 11:18:33 -0700 Message-ID: To: =?UTF-8?B?6rmA66+87ISd?= Cc: Brian Njenga , =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/mixed; boundary=001a114ac54ebae725054be29a79 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 18:18:39 -0000 --001a114ac54ebae725054be29a79 Content-Type: multipart/alternative; boundary=001a114ac54ebae721054be29a77 --001a114ac54ebae721054be29a77 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Minsuk, Attached are a few quick comments. I'll read more carefully this afternoon. I also have to read the references. Thanks, Dave On Wed, Mar 29, 2017 at 10:37 AM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: > Thank you so much =3D:) > > -Minsuk Kim > > Sent from my iPhone > > On 29 Mar 2017, at 12:12 PM, David Meyer wrote: > > > Hey Min-Suk, > > > On Wed, Mar 29, 2017 at 8:29 AM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: > >> Hi Dave, >> >> >> Thank you for giving me the great information. >> >> I absolutely agree your opinion that we need real ML data applied by dat= a >> pre-processing so that we have been already trying to make available ML >> data on many ways such as clustering and classification. (using datasets= of >> contents and URL) >> >> It's so challenge-able steps before using adaptive ML algorithm to >> network field. >> >> As you mentioned, RL is classical ML algorithm, but it is rapidly going >> develpment and make great results with tensorflow in many fields, >> unfortunately not network. >> >> For our tutorial, I attach some of practical examples with tensorflow as >> below, >> >> https://github.com/tensorflow/models >> >> >> Additionally, I submitted a personal draft to NMLRG even if it was close= d >> from last meeting. It's about collaborative distributed multi-agent usin= g >> re-inforcement learning and we trying to apply it to network real >> architecture. >> >> The attachment is on the email. I really appreciate giving me a small >> piece of your feedback and comment if you have a chance. >> > > Thanks. I will try to read/comment later today. > > Thanks again, > > Dave > > >> >> Sincerely, >> >> >> Min-Suk Kim >> >> Senior Researcher / Ph.D. >> Intelligent IoE Network Research Section, >> ETRI >> >> >> >> >> >> >> ------------------------------ >> *=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C : *"David Meyer" >> *=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C : *2017-03-29 23:25:48 ( +09:00 ) >> *=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C : *=EA=B9=80=EB=AF=BC=EC=84=9D >> *=EC=B0=B8=EC=A1=B0 : *Brian Njenga , J=C3=A9r= =C3=B4me Fran=C3=A7ois < >> jerome.francois@inria.fr>, Oscar Mauricio Caicedo Rendon < >> omcaicedo@unicauca.edu.co>, Sheng Jiang , >> idnet@ietf.org >> *=EC=A0=9C=EB=AA=A9 : *Re: [Idnet] Intelligence-Defined Network Architec= ture and Call >> for Interests >> >> >> Apparently you can't attach a .pptx. The attachment is here (pptx and >> pdf): >> >> >> http://www.1-4-5.net/~dmm/ml/misc/musings.pptx >> http://www.1-4-5.net/~dmm/ml/misc/musings.pdf >> >> >> >> >> Thx, >> >> >> Dave >> >> >> >> >> On Wed, Mar 29, 2017 at 7:17 AM, David Meyer wrote: >> >> >> >>> >>> Hey Min-Suk, >>> >>> >>> Totally agree we need to learn from our environment, and RL is a natura= l >>> approach. After all, the network is always changing, has adversaries, e= tc. >>> All of this means. among other things, that we can't make simplifying >>> assumptions like stationary distributions, iid data, .... So RL is one= way >>> to attack these problems, and the classic algorithms you mention below = are >>> certainly a reasonable approach (I've been working with policy gradient= s >>> [0], trying to model/adapt the two-player game approach of AlphaGo to >>> networking; the problem there is that we don't have a source of labeled >>> expert data like the KGS Go server (https://www.gokgs.com/) to build >>> the supervised policy network....). >>> >>> >>> You might also want to check out the recent "boot" of evolution >>> strategies as a black-box approach to RL (in particular no gradients). = See >>> [1], [2], [3]. There is also a ton of code around if you want to try = some >>> of this out (see e.g.,https://github.com/dennyb >>> ritz/reinforcement-learning; this one is in tensorflow). Finally, I've >>> attached a few summary slides with some of my musings on this topic fro= m >>> past talks. >>> >>> >>> Thanks, >>> >>> >>> Dave >>> >>> >>> [BTW, two player minimax games seem to be popping up everywhere: >>> AlphaGo, variational autoencoders [4], GANs [5], and many others; somet= hing >>> to thing about for our domain] >>> >>> >>> [0] https://papers.nips.cc/paper/1713-policy-gradient-method >>> s-for-reinforcement-learning-with-function-approximation.pdf >>> [1] https://blog.openai.com/evolution-strategies/ >>> [2] https://arxiv.org/pdf/1703.03864.pdf >>> [3] http://jmlr.csail.mit.edu/papers/volume15/wierstra14a/wi >>> erstra14a.pdf >>> [4] http://www.1-4-5.net/~dmm/ml/vae.pdf >>> [5] https://arxiv.org/pdf/1406.2661.pdf >>> >>> >>> >>> >>> On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF=BC=EC=84=9D wrote: >>> >>> >>> >>>> Hi Brian, >>>> >>>> >>>> >>>> As you mentioned by the prior email, anticipating network DDos >>>> attacks is really trendy issue to solve by ML techniques. >>>> >>>> We also make some efforts how to avoid fagile nodes by a trustworthy >>>> communication, that means quantifying trustworthiness of node with >>>> normalization of various requirements such as security function, bandw= idth >>>> and etc. >>>> >>>> We are freshly approaching in routing layer with confidence using our >>>> own requirements, TPD(Trust Policy Distribution) and TD(Trust Degree). >>>> These requirements are considered to be solved by Reinforcement Learni= ng >>>> (RL) that is one of the ML algorithms. RL is useful to control some of >>>> network policy about specific actions and states with reinforced and >>>> purnished rewards (+/-), but the problem is too slow to acquire satisi= fied >>>> performance. Other ways to say it, anormaly dectection and regression >>>> analysis might be both efficient approaching methods to solve the issu= es >>>> Dave mentioned. >>>> >>>> >>>> >>>> Best Regards, >>>> >>>> >>>> Min-Suk Kim >>>> >>>> Senior Researcher / Ph.D. >>>> Intelligent IoE Network Research Section, >>>> *E*lectronics and *T*elecommunications *R*esearch *I*nstitute (*ET**R* >>>> *I)* >>>> e-mail : mskim16@etri.re.kr >>>> http://www.etri.re.kr/ >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >> > > --001a114ac54ebae721054be29a77 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Minsuk,

Attached are a few quick co= mments. I'll read more carefully this afternoon. I also have to read th= e references.

Thanks,

Dav= e


On Wed, Mar 29, 2017 at 10:37 AM, =EA=B9=80=EB=AF=BC=EC=84=9D <mskim16@etri.re.kr> wrote:
Thank you so much =3D:)

-Minsuk Kim

Sent from my iPhone

On 29 Mar 2017, at 12:12 PM, David Meyer <dmm@1-4-5.net> wrote:


Hey=C2=A0Min-Suk,


On Wed, Mar 29, 2017 at 8:29 AM, =EA=B9=80=EB=AF= =BC=EC=84=9D <mskim16@etri.re.kr> wrote:

Hi Dave,


Thank you for giving me the= =C2=A0great information.=C2=A0

I=C2=A0absolutely agree your = opinion that we need real ML data applied by data pre-processing so that we= have been already trying to=C2=A0make=C2=A0available ML data on many ways = such as clustering and classification. (using=C2=A0datasets of contents and URL)

It's so challenge-able st= eps before using=C2=A0adaptive=C2=A0ML algorithm to network field.=C2=A0=C2= =A0

As you mentioned, RL=C2=A0is = classical ML algorithm, but it is rapidly going develpment and make great r= esults=C2=A0with tensorflow=C2=A0in many fields, unfortunately not network.=

For our tutorial, I attach so= me of practical examples with tensorflow as below,

https://github.com/tensorflow/mo= dels


Additionally, I submitted a p= ersonal draft to NMLRG even if it was closed from last meeting. It's=C2= =A0about collaborative distributed multi-agent using re-inforcement learnin= g and=C2=A0we=C2=A0trying to apply=C2=A0it to network real architecture.

The attachment is=C2=A0on the= email. I=C2=A0really appreciate giving me a=C2=A0small piece of=C2=A0your = feedback and comment=C2=A0if you=C2=A0have a chance.


Thanks. I will try to read/comment later today.

Thanks again,

Dave
=C2=A0


Sincerely,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Resear= cher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
ETRI=C2=A0

=C2=A0

=C2=A0

=C2=A0

=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C : "David Meyer"= <dmm@1-4-5.net&g= t;
=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C : 2017-03-29 23:25:48 ( += 09:00 )
=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C : =EA=B9=80=EB=AF=BC=EC= =84=9D <mskim16@= etri.re.kr>
=EC=B0=B8=EC=A1=B0 : Brian Njenga <iambrianmuhia@gmail.com>, J=C3= =A9r=C3=B4me Fran=C3=A7ois <jerome.francois@inria.fr>, Oscar Mauricio Caicedo = Rendon <o= mcaicedo@unicauca.edu.co>, Sheng Jiang <jiangsheng@huawei.com>, idnet@ietf.org <= idnet@ietf.org><= /div>
=EC=A0=9C=EB=AA=A9 : Re: [Idnet] Intelligence-Defined Network A= rchitecture and Call for Interests

=C2=A0
Apparently you can't attach a .pptx. The attachment is= here (pptx and pdf):

=C2=A0

On Wed, Mar 29, 2017 at 7:17 AM, David Meyer <dmm@1-4-5.net>= ; wrote:

=C2=A0

=C2=A0

Hey Min-Suk,

=C2=A0

Totally agree we need to learn from our environment, and RL is a natur= al approach. After all, the network is always changing, has adversaries, et= c. All of this means. among other things, =C2=A0that we can't make simp= lifying assumptions like stationary distributions, =C2=A0iid data, .... So RL is one way to attack these problems, and the cl= assic algorithms you mention below are certainly a reasonable approach (I&#= 39;ve been working with policy gradients [0], trying to model/adapt the two= -player game approach of AlphaGo to networking; the problem there is that we don't have a source of labeled expert dat= a like the KGS Go server (https://www.gokgs.com/) to build the supervised policy network....)= .=C2=A0

=C2=A0

You might also want to check out the recent "boot" of evolut= ion strategies as a black-box approach to RL (in particular no gradients). = See [1], =C2=A0[2], =C2=A0[3]. There is also a ton of code around if you wa= nt to try some of this out (see e.g.,https://github.com/dennyb= ritz/reinforcement-learning; this one is in tensorflow). Finally, I've attached a few summary slide= s with some of my musings on this topic from past talks.

=C2=A0

Thanks,

=C2=A0

Dave

=C2=A0

[BTW, two player minimax games seem to be popping up everywhere: Alpha= Go, variational autoencoders [4], GANs [5], and many others; something to t= hing about for our domain]

=C2=A0

[0]=C2=A0https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation.pdf
[5]=C2=A0https://arxiv.org/pdf/1406.2661.pdf

=C2=A0

=C2=A0

On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF= =BC=EC=84=9D <mskim16@etri.re.kr> wrote:

=C2=A0

Hi Brian,

=C2=A0

As you mentioned by the prior= email, anticipating network DDos attacks=C2=A0is=C2=A0really trendy issue = to solve by ML techniques.

We also make some efforts how= to=C2=A0avoid fagile nodes by=C2=A0a trustworthy communication, that means= quantifying trustworthiness of node=C2=A0with normalization=C2=A0of variou= s requirements such as security function,=C2=A0bandwidth and etc.

We are freshly approaching in= =C2=A0routing layer with confidence using our own requirements, TPD(Trust P= olicy Distribution) and TD(Trust Degree). These requirements are considered= to be solved by Reinforcement Learning (RL)=C2=A0that is one of the ML algorithms. RL is useful to control some o= f network=C2=A0policy about specific actions and states with reinforced and= purnished rewards (+/-), but the problem is too slow to=C2=A0acquire satis= ified performance. Other ways to=C2=A0say it,=C2=A0anormaly dectection and regression analysis=C2=A0might be=C2=A0both efficient=C2=A0= approaching methods to solve the issues Dave mentioned.

=C2=A0

Best Regards,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Resear= cher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
Electronics and=C2=A0Telecommun= ications=C2=A0R<= /span>esearch=C2=A0Institute (ET= <= span style=3D"COLOR:rgb(255,0,0)">RI)=
e-mail =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0: = =C2=A0mskim16@etri.re.kr

=C2=A0

=C2=A0

=C2=A0

=C2=A0



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--001a114ac54ebae725054be29a79-- From nobody Wed Mar 29 11:32:08 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 46709126B72 for ; Wed, 29 Mar 2017 11:32:07 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.221 X-Spam-Level: X-Spam-Status: No, score=-4.221 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id Jhx8XVUcSXn7 for ; Wed, 29 Mar 2017 11:32:05 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id BF2CB12941A for ; Wed, 29 Mar 2017 11:32:04 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml706-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DJX59841; Wed, 29 Mar 2017 18:32:01 +0000 (GMT) Received: from NKGEML412-HUB.china.huawei.com (10.98.56.73) by lhreml706-cah.china.huawei.com (10.201.108.47) with Microsoft SMTP Server (TLS) id 14.3.301.0; Wed, 29 Mar 2017 19:32:00 +0100 Received: from NKGEML515-MBS.china.huawei.com ([169.254.5.223]) by nkgeml412-hub.china.huawei.com ([10.98.56.73]) with mapi id 14.03.0235.001; Thu, 30 Mar 2017 02:31:54 +0800 From: Sheng Jiang To: "idnet@ietf.org" Thread-Topic: Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGqdGuUgAGvsj8= Date: Wed, 29 Mar 2017 18:31:54 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CD16656@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com>, <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> Accept-Language: en-GB, zh-CN, en-US Content-Language: en-GB X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.154.151] Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020202.58DBFDA1.02E0, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.5.223, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 01fc2218270f3d0aa986cb27c64e70fc Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 18:32:07 -0000 I am trying set up a meeting place for tomorrow morning 8am~9am, will send = the information later to on-site participants directly. Giving that the mee= techo is only for formal WG/RG meetings, I am afraid it is almost impossibl= e for remote participant. In other private meeting I was in on Tuesday, we = did use webex. But the quality is not good without professional meeting equ= ipment. It was bad to bridge four people on site with one remote participan= t.=0A= =0A= Regards,=0A= =0A= Sheng=0A= ________________________________________=0A= From: IDNET [idnet-bounces@ietf.org] on behalf of Sheng Jiang [jiangsheng@h= uawei.com]=0A= Sent: 29 March 2017 0:44=0A= To: idnet@ietf.org=0A= Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for= Interests=0A= =0A= Oops... An important typo. Try again:=0A= =0A= we may have an informal meeting to discuss some common interests and potent= ial future activities (not "only" activities in IETF, but also other STO or= experimental trails, etc.) on Thursday morning.=0A= =0A= Sheng=0A= ________________________________________=0A= From: IDNET [idnet-bounces@ietf.org] on behalf of Sheng Jiang [jiangsheng@h= uawei.com]=0A= Sent: 29 March 2017 0:29=0A= To: idnet@ietf.org=0A= Subject: [Idnet] Intelligence-Defined Network Architecture and Call for Int= erests=0A= =0A= Hi, all,=0A= =0A= Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.=0A= =0A= https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-def= ined-network-01.pdf=0A= =0A= Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may have an informal meeting to disc= uss some common interests and potential future activities (not any activiti= es in IETF, but also other STO or experimental trails, etc.) on Thursday m= orning.=0A= =0A= FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.=0A= =0A= https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844=0A= https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D510= 11=0A= =0A= Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.=0A= =0A= Best regards,=0A= =0A= Sheng=0A= _______________________________________________=0A= IDNET mailing list=0A= IDNET@ietf.org=0A= https://www.ietf.org/mailman/listinfo/idnet=0A= =0A= _______________________________________________=0A= IDNET mailing list=0A= IDNET@ietf.org=0A= https://www.ietf.org/mailman/listinfo/idnet=0A= From nobody Wed Mar 29 11:37:23 2017 Return-Path: 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"idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSqLt+otmNK6LAE0GQptHszQd4tQ== Date: Wed, 29 Mar 2017 18:37:18 +0000 Message-ID: Accept-Language: en-US Content-Language: en-US X-MS-Has-Attach: X-MS-TNEF-Correlator: user-agent: Microsoft-MacOutlook/f.1a.0.160910 x-ms-exchange-messagesentrepresentingtype: 1 x-ms-exchange-transport-fromentityheader: Hosted x-originating-ip: [10.155.153.81] Content-Type: text/plain; charset="utf-8" Content-ID: <5695D085D009B1429F4CD09250A77C09@emea.cisco.com> Content-Transfer-Encoding: base64 MIME-Version: 1.0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 18:37:22 -0000 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:message-id:subject:to:cc; bh=tDcT3+0lRkSpQfb6VSzwklvTF8bo79L6anKrcbQ88LI=; b=FnsGDp+KkTB+FBwiEnBnjCZssz3vRo1aBUKGhNnxHPRNBhmk/XuCq4Vq8FVHKGv6DN aYbIeQBWio0qbWS34Cl9CZx+KUAmwNQeCUZ7KKR5p9MP1IUDuaCONxCifkpzcC1T7wRd YJlS+O9cMMevAK/vzckb1KkUlPTXlomWfJ5BGo1uhLZ7iNSnEszj3G5jpm8v+tFa3h/g CX5lI7CoJ01JMgEGdXTD0txPCr/rIPu5cDGDa89jeJp9tlyn+ZGfD8wwyCE+FiEn9tKk o+XtaHIjQlu44e9Htu3tsKtDbaH75UHcaxHBiO5/sk7RWaLFc+D2P6WpHLcIUnMxT75+ xAwg== X-Gm-Message-State: AFeK/H1zY5P9RHSjEG+FIufmz6Zu0lnCqdw/m28yC9k3+JanATxzhAZ29usiqCFa8zpiL3adAeEH4OQyeHVwoA== X-Received: by 10.200.45.59 with SMTP id n56mr2137273qta.137.1490813640877; Wed, 29 Mar 2017 11:54:00 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.174.118 with HTTP; Wed, 29 Mar 2017 11:53:20 -0700 (PDT) In-Reply-To: References: From: =?UTF-8?Q?Jo=C3=A3o_Paulo_S=2E_Medeiros?= Date: Wed, 29 Mar 2017 15:53:20 -0300 Message-ID: To: David Meyer Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a1142ae367db7b0054be319d0 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 18:54:12 -0000 --001a1142ae367db7b0054be319d0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear David Meyer and all, I would like to contribute with some ideas and published works from my academic education. I have been working with Machine Learning (ML) and Computer Network since 2007. I initially started my research interests with the use of neural networks to aid the performance of classification and characterization of remote computer fingerprinting (e.g. [0] [1], and most recently [2] [3]). Although it is not my current main line of research, my experience will agree with the David's comment that ``we need to think about is publicly available standardized data''. This probably is one of the main problems for researchers trying to advance or reproduce state-of-the-art research on Intrusion Detection systems (and others feature extraction + pattern recognition tasks) using ML. My current main line of research is related to two of David's concerns: namely, (i) the UTON and the (ii) Controllability of Computer Networks. My PhD thesis work was related to the use of model which could be used to minimize the overhead of network monitoring. My last published work about this is in [4]. I used the theory of Complex Networks Controllability [5] to achieve my PhD goal. However, I realized that its too more practical to use this theory to build Observable (dual problem) network monitoring systems with minimal sensor nodes, since in controllability we need to directly change (or induce) the state of network devices. In this sense, the theory of Adaptive Filtering (e.g. Kalman Filter) is important too. Even so, Controlability of computer networks it's still a very interesting and challenging problem involving not only Complex Networks theory, but also, probably, Markov Process and ML. Still about UTON, the network topology almost always plays an important role in the ML system design. For many reasons, the topology is not available and its estimation is also another important problem we could approach using ML [6]. Finally, I would like to share an inspiring paper entitled ``Mathematics and the Internet: A Source of Enormous Confusion and Great Potential'' [7]. Best regards! [0] http://dx.doi.org/10.1109/EFTA.2007.4416854 [1] http://dx.doi.org/10.1007/978-3-540-89173-4_20 [2] http://dx.doi.org/10.1007/978-3-319-05885-6_12 [3] http://dx.doi.org/10.1201/b17333-10 [4] http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.15 [5] http://dx.doi.org/10.1038/nature10011 [6] http://dx.doi.org/10.1109/TNET.2011.2175747 [7] http://www.ams.org/notices/200905/tx090500586p.pdf -- Prof. Jo=C3=A3o Paulo Souza Medeiros On Wed, Mar 22, 2017 at 2:29 PM, David Meyer wrote: > Folks, > > I thought I'd try to get some discussion going by outlining some of my > views as to why networking is lagging other areas in the development and > application of Machine Learning (ML). In particular, networking is way > behind what we might call the "perceptual tasks" (vision, NLP, robotics, > etc) as well as other areas (medicine, finance, ...). The attached slide > from one of my decks tries to summarize the situation, but I'll give a bi= t > of an outline below. > > So why is networking lagging many other fields when it comes to the > application of machine learning? There are several reasons which I'll try > to outline here (I was fortunate enough to discuss this with the > packetpushers crew a few weeks ago, see [0]). These are in no particular > order. > > First, we don't have a "useful" theory of networking (UTON). One way to > think about what such a theory would look like is by analogy to what we s= ee > with the success of convolutional neural networks (CNNs) not only for > vision but now for many other tasks. In that case there is a theory of ho= w > vision works, built up from concepts like receptive fields, shared weight= s, > simple and complex cells, etc. For example, the input layer of a CNN isn'= t > fully connected; rather connections reflect the receptive field of the > input layer, which is in a way that is "inspired" by biological vision > (being very careful with "biological inspiration"). Same with the > alternation of convolutional and pooling layers; these loosely model the > alternation of simple and complex cells in the primary visual cortex (V1)= , > the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a > theory seems to be required for transfer learning [1], which we'll need i= f > we don't want every network to be analyzed in an ad-hoc, one-off style > (like we see today). > > The second thing that we need to think about is publicly available > standardized data sets. Examples here include MNIST, ImageNet, and many > others. The result of having these data sets has been the steady ratcheti= ng > down of error rates on tasks such as object and scene recognition, NLP, a= nd > others to super-human levels. Suffice it to say we have nothing like thes= e > data sets for networking. Networking data sets today are largely > proprietary, and because there is no UTON, there is no real way to compar= e > results between them. > > Third, there is a large skill set gap. Network engineers (us!) typically > don't have the mathematical background required to build effective machin= e > learning at scale. See [2] for an outline of some of the mathematical > skills that are essential for effective ML. There is a lot more to this, > involving how progress is made in ML (open data, open source, open models= , > in general open science and associated communities, see e.g., OpenAi [3], > Distill [4], and many others). In any event we need build community and > gain new skills if we want to be able to develop and apply state of the a= rt > machine learning algorithms to network data, at scale. The bottom line is > that it will be difficult if not impossible to be effective in the ML spa= ce > if we ourselves don't understand how it works and further, if we can buil= d > explainable systems (noting that explaining what the individual neurons i= n > a deep neural network are doing is notoriously difficult; that said much > progress is being made). So we want to build explainable, end-to-end > trained systems, and to accomplish this we ourselves need to understand h= ow > these algorithms work, but in training and in inference. > > This email is already TL;DR but I'll add one more here: We need to learn > control, not just prediction. Since we live in an inherently adversarial > environment we need to take advantage of Reinforcement Learning as well a= s > the various attacks being formulated against ML; [5] gives one interestin= g > example of attacks against policy networks using adversarial examples. Se= e > also slides 31 and 32 of [6] for some more on this topic. > > I hope some of this gets us thinking about the problems we need to solve > in order to be successful in the ML space. There's plenty more of this on > http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. > I'm looking forward to the discussion. > > Thanks, > > --dmm > > > > > [0] http://packetpushers.net/podcast/podcasts/pq-show-107- > applicability-machine-learning-networking/ > > [1] http://sebastianruder.com/transfer-learning/index.html > [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ > [3] https://openai.com/blog/ > [4] http://distill.pub/ > [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf > [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --001a1142ae367db7b0054be319d0 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Dear David Meyer and all,
=

I would lik= e to contribute with some ideas and published works from my academic educat= ion.

I= have been working with Machine Learning (ML) and Computer Network since 20= 07. I initially=C2=A0started my research interests with the use of neural n= etworks to aid the performance of classification and characterization of re= mote computer fingerprinting (e.g. [0] [1], and most recently [2] [3]). Alt= hough it is not my current main line of research, my experience will agree = with the David's comment that ``we nee= d to think about is publicly available standardized data''. = This probably is one of the main problems for researchers trying to advance= or reproduce state-of-the-art research on Intrusion Detection systems (and= others feature extraction + pattern recognition tasks) using ML.

My current main= line of=C2=A0research is related to two of David's concerns: namely, (= i) the=C2=A0UTON and the (ii) Controllabil= ity of Computer Networks. My PhD thesis work was related to the use of mode= l which could be used to minimize the overhead of network monitoring. My la= st published work about this is in [4]. I used the theory of Complex Networ= ks Controllability [5] to achieve my PhD goal.=C2=A0However, I realized that its too more practical to use this theory = to build Observable (dual problem) network monitoring systems with minimal = sensor nodes, since in controllability we need to directly change (or induc= e) the state of network devices. In this sense, the theory of Adaptive Filt= ering (e.g. Kalman Filter) is important too. Even so, Controlability of com= puter networks it's still a very interesting and challenging=C2=A0probl= em involving not only Complex Networks theory, but also, probably, Markov P= rocess and ML.

Still about UTON, the networ= k topology almost always plays an important role in the ML system design. F= or many reasons, the topology is not available and its estimation is also a= nother important problem we could approach using ML [6].

Finally, I would like to= share an inspiring paper entitled ``Mathematics and the Internet: A Source= of Enormous Confusion and Great Potential'' [7].

Best regards!


Hey=C2=A0Min-Suk,


On Wed, Mar 29, 2017 at 8:2= 9 AM, =EA=B9=80=EB=AF=BC=EC=84=9D <mskim16@etri.re.kr> wrot= e:

Hi Dave,


Thank you for giving me the= =C2=A0great information.=C2=A0

I=C2=A0absolutely agree your = opinion that we need real ML data applied by data pre-processing so that we= have been already trying to=C2=A0make=C2=A0available ML data on many ways = such as clustering and classification. (using=C2=A0datasets of contents and URL)

It's so challenge-able st= eps before using=C2=A0adaptive=C2=A0ML algorithm to network field.=C2=A0=C2= =A0

As you mentioned, RL=C2=A0is = classical ML algorithm, but it is rapidly going develpment and make great r= esults=C2=A0with tensorflow=C2=A0in many fields, unfortunately not network.=

For our tutorial, I attach so= me of practical examples with tensorflow as below,

https://github.com/tensorflow/mo= dels


Additionally, I submitted a p= ersonal draft to NMLRG even if it was closed from last meeting. It's=C2= =A0about collaborative distributed multi-agent using re-inforcement learnin= g and=C2=A0we=C2=A0trying to apply=C2=A0it to network real architecture.

The attachment is=C2=A0on the= email. I=C2=A0really appreciate giving me a=C2=A0small piece of=C2=A0your = feedback and comment=C2=A0if you=C2=A0have a chance.

<= /div>

Thanks. I will try to read/comm= ent later today.

Thanks again,

Dave
=C2=A0


Sincerely,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Resear= cher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
ETRI=C2=A0

=C2=A0

=C2=A0

=C2=A0

=EB=B3=B4=EB=82=B8 =EC=82=AC=EB=9E=8C : "David Meyer"= <dmm@1-4-5.net&g= t;
=EB=B3=B4=EB=82=B8 =EB=82=A0=EC=A7=9C : 2017-03-29 23:25:48 ( += 09:00 )
=EB=B0=9B=EB=8A=94 =EC=82=AC=EB=9E=8C : =EA=B9=80=EB=AF=BC=EC= =84=9D <mskim16@= etri.re.kr>
=EC=B0=B8=EC=A1=B0 : Brian Njenga <iambrianmuhia@gmail.com>, J=C3= =A9r=C3=B4me Fran=C3=A7ois <jerome.francois@inria.fr>, Oscar Mauricio Caicedo = Rendon <o= mcaicedo@unicauca.edu.co>, Sheng Jiang <jiangsheng@huawei.com>, idnet@ietf.org <idnet@ietf.org>
=EC=A0=9C=EB=AA=A9 : Re: [Idnet] Intelligence-Defined Network A= rchitecture and Call for Interests

=C2=A0

Apparently you can't attach a .pptx. The attach= ment is here (pptx and pdf):

=C2=A0

On Wed, Mar 29, 2017 at 7:17 AM, David Meyer <dmm@1-4-5.net>= ; wrote:

=C2=A0

=C2=A0

Hey Min-Suk,

=C2=A0

Totally agree we need to learn from our environment, and RL is a natur= al approach. After all, the network is always changing, has adversaries, et= c. All of this means. among other things, =C2=A0that we can't make simp= lifying assumptions like stationary distributions, =C2=A0iid data, .... So RL is one way to attack these problems, and the cl= assic algorithms you mention below are certainly a reasonable approach (I&#= 39;ve been working with policy gradients [0], trying to model/adapt the two= -player game approach of AlphaGo to networking; the problem there is that we don't have a source of labeled expert dat= a like the KGS Go server (https://www.gokgs.com/) to build the supervised policy network....)= .=C2=A0

=C2=A0

You might also want to check out the recent "boot" of evolut= ion strategies as a black-box approach to RL (in particular no gradients). = See [1], =C2=A0[2], =C2=A0[3]. There is also a ton of code around if you wa= nt to try some of this out (see e.g.,https://github.com/dennyb= ritz/reinforcement-learning; this one is in tensorflow). Finally, I've attached a few summary slide= s with some of my musings on this topic from past talks.

=C2=A0

Thanks,

=C2=A0

Dave

=C2=A0

[BTW, two player minimax games seem to be popping up everywhere: Alpha= Go, variational autoencoders [4], GANs [5], and many others; something to t= hing about for our domain]

=C2=A0

[5]=C2=A0https://arxiv.org/pdf/1406.2661.pdf

=C2=A0

=C2=A0

On Tue, Mar 28, 2017 at 4:04 PM, =EA=B9=80=EB=AF= =BC=EC=84=9D <mskim16@etri.re.kr> wrote:

=C2=A0

Hi Brian,

=C2=A0

As you mentioned by the prior= email, anticipating network DDos attacks=C2=A0is=C2=A0really trendy issue = to solve by ML techniques.

We also make some efforts how= to=C2=A0avoid fagile nodes by=C2=A0a trustworthy communication, that means= quantifying trustworthiness of node=C2=A0with normalization=C2=A0of variou= s requirements such as security function,=C2=A0bandwidth and etc.

We are freshly approaching in= =C2=A0routing layer with confidence using our own requirements, TPD(Trust P= olicy Distribution) and TD(Trust Degree). These requirements are considered= to be solved by Reinforcement Learning (RL)=C2=A0that is one of the ML algorithms. RL is useful to control some o= f network=C2=A0policy about specific actions and states with reinforced and= purnished rewards (+/-), but the problem is too slow to=C2=A0acquire satis= ified performance. Other ways to=C2=A0say it,=C2=A0anormaly dectection and regression analysis=C2=A0might be=C2=A0both efficient=C2=A0= approaching methods to solve the issues Dave mentioned.

=C2=A0

Best Regards,

=C2=A0

Min-Suk Kim
=C2=A0
Senior Resear= cher / Ph.D.
Intelligent IoE Network Research Section,=C2=A0
Electronics and=C2=A0Telecommun= ications=C2=A0R<= /span>esearch=C2=A0Institute (ET= <= span style=3D"COLOR:rgb(255,0,0)">RI)=
e-mail =C2=A0 =C2=A0 =C2=A0 =C2=A0 =C2=A0: = =C2=A0mskim16@etri.re.kr

=C2=A0

=C2=A0

=C2=A0

=C2=A0


[0]=C2=A0http://dx.doi.org/10.11= 09/EFTA.2007.4416854
[1]=C2=A0http://dx.doi.org/10.10= 07/978-3-540-89173-4_20
[2]=C2=A0http://dx.doi.org/10.1= 007/978-3-319-05885-6_12
[3]=C2=A0http://dx.doi.org/10.1201/b17333= -10
[4]=C2=A0http://dx.doi.org/10.1109/CIT/IUCC/= DASC/PICOM.2015.15
[5] http://dx.doi.org/10.1038/nature10011=
[6] http://dx.doi.org/10.1109/TNET.2011.2175747
[7]=C2=A0http://www.ams.org/notices/200905/tx090500586p.pdf<= /div>

-- Prof. Jo=C3=A3o Paulo = Souza Medeiros

On Wed, Mar 22, 2017 at 2:29 PM, David Meyer= <d= mm@1-4-5.net> wrote:
Folks,

I though= t I'd try to get some discussion going by outlining some of my views as= to why networking is lagging other areas in the development and applicatio= n of Machine Learning (ML). In particular, networking is way behind what we= might call the "perceptual tasks" (vision, NLP, robotics, etc) a= s well as other areas (medicine, finance, ...). The attached slide from one= of my decks tries to summarize the situation, but I'll give a bit of a= n outline below.=C2=A0

=
So why is networking lagging many other = fields when it comes to the application of machine learning? There are seve= ral reasons which I'll try to outline here (I was fortunate enough to d= iscuss this with the packetpushers crew a few weeks ago, see [0]). These ar= e in no particular order.

First, we don't have a "usef= ul" theory of networking (UTON). One way to think about what such a th= eory would look like is by analogy to what we see with the success of convo= lutional neural networks (CNNs) not only for vision but now for many other = tasks. In that case there is a theory of how vision works, built up from co= ncepts like receptive fields, shared weights, simple and complex cells, etc= . For example, the input layer of a CNN isn't fully connected; rather c= onnections reflect the receptive field of the input layer, which is in a wa= y that is "inspired" by biological vision (being very careful wit= h "biological inspiration"). Same with the alternation of convolu= tional and pooling layers; these loosely model the alternation of simple an= d complex cells in the primary visual cortex (V1), the secondary visual cor= tex(V2) and the Brodmann area (V3).=C2=A0BTW, such a theory seems to be req= uired for transfer learning [1], which we'll need if we don't want = every network to be analyzed in an ad-hoc, one-off style (like we see today= ).

The second thing that we need to think about is publicly ava= ilable standardized data sets. Examples here include MNIST, ImageNet, and m= any others. The result of having these data sets has been the steady=C2=A0r= atcheting down of error rates on tasks such as object and scene recognition= , NLP, and others to super-human levels. Suffice it to say we have nothing = like these data sets for networking. Networking data sets today are largely= proprietary, and because there is no UTON, there is no real way to compare= results between them.

=
Third, there is a large skill set gap. N= etwork engineers (us!) typically don't have the mathematical background= required to build effective machine learning at scale. See [2] for an outl= ine of some of the mathematical skills that are essential for effective ML.= There is a lot more to this, involving how progress is made in ML (open da= ta, open source, open models, in general open science and associated commun= ities, see e.g., OpenAi [3], Distill [4], and many others). In any event we= need build community and gain new skills if we want to be able to develop = and apply state of the art machine learning algorithms to network data, at = scale. The bottom line is that it will be difficult if not impossible to be= effective in the ML space if we ourselves don't understand how it work= s and further, if we can build explainable systems (noting that explaining = what the individual neurons in a deep neural network are doing is notorious= ly difficult; that said much progress is being made). So we want to build e= xplainable, end-to-end trained systems, and to accomplish this we ourselves= need to understand how these algorithms work, but in training and in infer= ence.

This email is already TL;DR but I'll add one more her= e: We need to learn control, not just prediction. Since we live in an inher= ently adversarial environment we need to take advantage of Reinforcement Le= arning as well as the various attacks being formulated against ML; [5] give= s one interesting example of attacks against policy networks using adversar= ial examples. See also slides 31 and 32 of [6] for some more on this topic.=

I hope some of this gets us thinking about the problems we nee= d to solve in order to be successful in the ML space. There's plenty mo= re of this on ht= tp://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html.
I'm looking forward to the discus= sion.

Thanks,

--dmm




_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


--001a1142ae367db7b0054be319d0-- From nobody Wed Mar 29 12:01:23 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 091081296E3 for ; Wed, 29 Mar 2017 12:01:21 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.201 X-Spam-Level: X-Spam-Status: No, score=-4.201 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id T7y_l-h7Yu_z for ; Wed, 29 Mar 2017 12:01:18 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 8FE0C129541 for ; Wed, 29 Mar 2017 12:01:00 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v2TJ0xpp039630 for ; Thu, 30 Mar 2017 04:00:59 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw2.nict.go.jp with ESMTP id v2TJ0xoS039565 for ; Thu, 30 Mar 2017 04:00:59 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id 409E9A071 for ; Thu, 30 Mar 2017 04:00:59 +0900 (JST) Date: Thu, 30 Mar 2017 04:00:54 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170329190053.GX4808@spectre> References: MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 19:01:21 -0000 Dear Joao, Thank you for your contribution. To extend its reach, as I have previously requested, please add it to the repository or give us permission to do so. Thank you. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Wed Mar 29 12:08:21 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id AB8191296BD for ; Wed, 29 Mar 2017 12:08:19 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.448 X-Spam-Level: X-Spam-Status: No, score=-2.448 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_ENVFROM_END_DIGIT=0.25, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id Nv1lA3fZ_Kiv for ; Wed, 29 Mar 2017 12:08:17 -0700 (PDT) Received: from mail-qk0-x22b.google.com (mail-qk0-x22b.google.com [IPv6:2607:f8b0:400d:c09::22b]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5CE6C1296D1 for ; Wed, 29 Mar 2017 12:08:17 -0700 (PDT) Received: by mail-qk0-x22b.google.com with SMTP id r142so21715959qke.2 for ; Wed, 29 Mar 2017 12:08:17 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=H3qnSAQc7AteWRpLpiXuBxnW12ZznNhcfKgugjsP1nw=; b=bQawm14WHgAsK0QyTsQWd5IhHEtCTKAVtcAeQwE4GaZ7GJ+JprRLlY1BxyW1Qa9P0K 5+aw2GfuLTDUqqhIMl+dobB+zXt5K33YlxD+oDVWgsN0CLLLLgWqFaqlLHW29bPu4bHV 7LsYPVJDm1TIPNsQKPQjcngi8EebAw9V0XzIOiiWYZfU5uyKU2myW1ioP30r6Zv90gks kprFWlpgCMqAzmdyNTQm1A/lbH08AGo048SF+UfYJgtSEu85lhthAOpDrGVvnm+1iii1 KysaOwBEzImIt/0YVC+0MqA7p5cOTB/K1NH9LZwvffTYEj9f78aCcT7PBt/l/fcQPu64 CHDw== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=H3qnSAQc7AteWRpLpiXuBxnW12ZznNhcfKgugjsP1nw=; b=FXo6vi3dBEgLE+RhAv6OYqarIKQLaeLx6uqbxNheem0v7PFZVu6iFVqPrwu8163pHc EmzX3aDDQ8V6AMhc0ZvREhvEu19XnTjmrNR0OMjEqhR/DkUa5H/w12yjhHPxIdA/WN2Z quXe9LOCRfJ93xQzA1qnntlZdnEj9O5WHUYgloFHKlI5uDEjjIz8/b1m6JfHtnF+g0ZK PvK4xgLgGh8mEeyfClLYx71AvJIX19C2JDdsW/Sm+CpmRCDmynVLcgit0gMhFe0Yki3e YWTUBAMbIm9AtmKgHyRGcKLdjQ+q0u/DUdoFe/dyPbMFBl/HlCkrqAHtseH+WsrOhlQz kVHQ== X-Gm-Message-State: AFeK/H326iFKRknc7DDT7umVRF9A/x4IIoD2Ne6xQSIoLt1Cm65UYXgI+80KY6aZZcKRn8BJHAnQNcDhHmOmdg== X-Received: by 10.55.115.67 with SMTP id o64mr2310256qkc.215.1490814496234; Wed, 29 Mar 2017 12:08:16 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.174.118 with HTTP; Wed, 29 Mar 2017 12:07:35 -0700 (PDT) In-Reply-To: <20170329190053.GX4808@spectre> References: <20170329190053.GX4808@spectre> From: =?UTF-8?Q?Jo=C3=A3o_Paulo_S=2E_Medeiros?= Date: Wed, 29 Mar 2017 16:07:35 -0300 Message-ID: To: Pedro Martinez-Julia Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a114ff3b679648a054be34c07 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 19:08:20 -0000 --001a114ff3b679648a054be34c07 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Dear Pedro, you have my permission to do so. Thank you. -- Prof. Jo=C3=A3o Paulo Souza Medeiros On Wed, Mar 29, 2017 at 4:00 PM, Pedro Martinez-Julia wrote: > Dear Joao, > > Thank you for your contribution. To extend its reach, as I have > previously requested, please add it to the repository or give us > permission to do so. Thank you. > > Regards, > Pedro > > -- > Pedro Martinez-Julia > Network Science and Convergence Device Technology Laboratory > Network System Research Institute > National Institute of Information and Communications Technology (NICT) > 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan > Email: pedro@nict.go.jp > --------------------------------------------------------- > *** Entia non sunt multiplicanda praeter necessitatem *** > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a114ff3b679648a054be34c07 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Dear Pedro,

you have my permission to d= o so. Thank you.

-- Prof. Jo=C3=A3o Paulo Souza Medeiros

On Wed, Mar 29, 2017 at 4:00 PM, Pedro Marti= nez-Julia <pedro@nict.go.jp> wrote:
Dear Joao,

Thank you for your contribution. To extend its reach, as I have
previously requested, please add it to the repository or give us
permission to do so. Thank you.

Regards,
Pedro

--
Pedro Martinez-Julia
Network Science and Convergence Device Technology Laboratory
Network System Research Institute
National Institute of Information and Communications Technology (NICT)
4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Email: pedro@nict.go.jp
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--001a114ff3b679648a054be34c07-- From nobody Wed Mar 29 12:15:10 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id EF67C129510 for ; Wed, 29 Mar 2017 12:15:08 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.898 X-Spam-Level: X-Spam-Status: No, score=-1.898 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id yIxg3Q1wfv3X for ; Wed, 29 Mar 2017 12:15:03 -0700 (PDT) Received: from mail-qt0-x233.google.com (mail-qt0-x233.google.com [IPv6:2607:f8b0:400d:c0d::233]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id A900012714F for ; Wed, 29 Mar 2017 12:15:03 -0700 (PDT) Received: by mail-qt0-x233.google.com with SMTP id n21so21451229qta.1 for ; Wed, 29 Mar 2017 12:15:03 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=ZVtFZwdbog60GsUpI3lAj1n1K7BkT+A/5gxd9Cs5uOo=; b=iNtV1xI6zugHfv8dUfrsD+KRiFxqZ/FIbBj3OOeowTIc1oZSrN5X9UUGP8IfNHJ6fW 07rgWw0zzX1zDXUn7E2Uz5NhQn8wO2x7srp94Z7sJau5Q+EL3925YcScSc2a8BPE2K2C E4KwTdjfL8r/VBXjrq6PsLXLuNQZc/0jtqYKVp3rp0cIKxTIEf1/dX9iDd4GfxFz/f3z UCKPx7r3NdkbBBc45sR9Wo6oHzCbpJS/+f0gxpRt26GKPdhYu1OYqLQR4NGygcEmF8cA JXpXZ8s93UiU9SNxfuHOuUXfthT7zpgEpPTYY41hjV/A4I1dARK/HNdxVCrC2gtPVsni pckQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=ZVtFZwdbog60GsUpI3lAj1n1K7BkT+A/5gxd9Cs5uOo=; b=ViHVofKI0w/7lt/gmF7fqSH302l02kANdR4I6rKJfTt2opA+jBO9hGPVMxIB/5uRNl QPXYgAj6Rah/0+wmBwt2aKSU7X4QcRhG4FABPHncj0IZJcq0gvLuZRte2S35NFdugtln IbDFyuTHSeV0vNz/otmvWC53Y1LWHhy1zlmCVRAFv58AV3tdEQVGl/vSkeq+8JJVoEb7 Q6EcWULcM7nYcakPHUqsiNbX3rhSIAazOJQFn5sdRvVBAEFj1TcsRPabC3qCgkENRP8z FWjWo4AXBuI/AVYDxb/jAQRo2VAMTytqfDjmPPpAH1ifuRkPGoS2DGE9zs9uTNe9E1a/ cP7w== X-Gm-Message-State: AFeK/H1CJJJ1CmQuyvvfRf9ZChx7LujKNB4BEQ+82rL4aUM60/VLSUzxyLIQvSNaClId8SGlrV3W1O2OjzDNuQ== X-Received: by 10.200.47.9 with SMTP id j9mr2428867qta.57.1490814902465; Wed, 29 Mar 2017 12:15:02 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Wed, 29 Mar 2017 12:15:01 -0700 (PDT) X-Originating-IP: [67.189.0.51] In-Reply-To: References: From: David Meyer Date: Wed, 29 Mar 2017 12:15:01 -0700 Message-ID: To: =?UTF-8?Q?Jo=C3=A3o_Paulo_S=2E_Medeiros?= Cc: list-idnet Content-Type: multipart/alternative; boundary=001a113773b4b03668054be36485 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 19:15:09 -0000 --001a113773b4b03668054be36485 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Jo=C3=A3o, Thanks for the references. More to read and understand! I'll just pint out here that ``Mathematics and the Internet: A Source of Enormous Confusion and Great Potential'' was written by two of my good friends and colleagues (John and Walter). See http://www.1-4-5.net/~dmm/ml/talks/2017/nanog61.pptx for some of the work John and I have done. There's lots more of this stuff on http://www.1-4-5.net/~dmm/vita.hml under Recent Talks. Small world! Dave On Wed, Mar 29, 2017 at 11:53 AM, Jo=C3=A3o Paulo S. Medeiros wrote: > Dear David Meyer and all, > > I would like to contribute with some ideas and published works from my > academic education. > > I have been working with Machine Learning (ML) and Computer Network since > 2007. I initially started my research interests with the use of neural > networks to aid the performance of classification and characterization of > remote computer fingerprinting (e.g. [0] [1], and most recently [2] [3]). > Although it is not my current main line of research, my experience will > agree with the David's comment that ``we need to think about is publicly > available standardized data''. This probably is one of the main problems > for researchers trying to advance or reproduce state-of-the-art research = on > Intrusion Detection systems (and others feature extraction + pattern > recognition tasks) using ML. > > My current main line of research is related to two of David's concerns: > namely, (i) the UTON and the (ii) Controllability of Computer Networks. > My PhD thesis work was related to the use of model which could be used to > minimize the overhead of network monitoring. My last published work about > this is in [4]. I used the theory of Complex Networks Controllability [5] > to achieve my PhD goal. However, I realized that its too more practical > to use this theory to build Observable (dual problem) network monitoring > systems with minimal sensor nodes, since in controllability we need to > directly change (or induce) the state of network devices. In this sense, > the theory of Adaptive Filtering (e.g. Kalman Filter) is important too. > Even so, Controlability of computer networks it's still a very interestin= g > and challenging problem involving not only Complex Networks theory, but > also, probably, Markov Process and ML. > > Still about UTON, the network topology almost always plays an important > role in the ML system design. For many reasons, the topology is not > available and its estimation is also another important problem we could > approach using ML [6]. > > Finally, I would like to share an inspiring paper entitled ``Mathematics > and the Internet: A Source of Enormous Confusion and Great Potential'' [7= ]. > > Best regards! > > [0] http://dx.doi.org/10.1109/EFTA.2007.4416854 > [1] http://dx.doi.org/10.1007/978-3-540-89173-4_20 > [2] http://dx.doi.org/10.1007/978-3-319-05885-6_12 > [3] http://dx.doi.org/10.1201/b17333-10 > [4] http://dx.doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.15 > [5] http://dx.doi.org/10.1038/nature10011 > [6] http://dx.doi.org/10.1109/TNET.2011.2175747 > [7] http://www.ams.org/notices/200905/tx090500586p.pdf > > -- Prof. Jo=C3=A3o Paulo Souza Medeiros > > On Wed, Mar 22, 2017 at 2:29 PM, David Meyer wrote: > >> Folks, >> >> I thought I'd try to get some discussion going by outlining some of my >> views as to why networking is lagging other areas in the development and >> application of Machine Learning (ML). In particular, networking is way >> behind what we might call the "perceptual tasks" (vision, NLP, robotics, >> etc) as well as other areas (medicine, finance, ...). The attached slide >> from one of my decks tries to summarize the situation, but I'll give a b= it >> of an outline below. >> >> So why is networking lagging many other fields when it comes to the >> application of machine learning? There are several reasons which I'll tr= y >> to outline here (I was fortunate enough to discuss this with the >> packetpushers crew a few weeks ago, see [0]). These are in no particular >> order. >> >> First, we don't have a "useful" theory of networking (UTON). One way to >> think about what such a theory would look like is by analogy to what we = see >> with the success of convolutional neural networks (CNNs) not only for >> vision but now for many other tasks. In that case there is a theory of h= ow >> vision works, built up from concepts like receptive fields, shared weigh= ts, >> simple and complex cells, etc. For example, the input layer of a CNN isn= 't >> fully connected; rather connections reflect the receptive field of the >> input layer, which is in a way that is "inspired" by biological vision >> (being very careful with "biological inspiration"). Same with the >> alternation of convolutional and pooling layers; these loosely model the >> alternation of simple and complex cells in the primary visual cortex (V1= ), >> the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a >> theory seems to be required for transfer learning [1], which we'll need = if >> we don't want every network to be analyzed in an ad-hoc, one-off style >> (like we see today). >> >> The second thing that we need to think about is publicly available >> standardized data sets. Examples here include MNIST, ImageNet, and many >> others. The result of having these data sets has been the steady ratchet= ing >> down of error rates on tasks such as object and scene recognition, NLP, = and >> others to super-human levels. Suffice it to say we have nothing like the= se >> data sets for networking. Networking data sets today are largely >> proprietary, and because there is no UTON, there is no real way to compa= re >> results between them. >> >> Third, there is a large skill set gap. Network engineers (us!) typically >> don't have the mathematical background required to build effective machi= ne >> learning at scale. See [2] for an outline of some of the mathematical >> skills that are essential for effective ML. There is a lot more to this, >> involving how progress is made in ML (open data, open source, open model= s, >> in general open science and associated communities, see e.g., OpenAi [3]= , >> Distill [4], and many others). In any event we need build community and >> gain new skills if we want to be able to develop and apply state of the = art >> machine learning algorithms to network data, at scale. The bottom line i= s >> that it will be difficult if not impossible to be effective in the ML sp= ace >> if we ourselves don't understand how it works and further, if we can bui= ld >> explainable systems (noting that explaining what the individual neurons = in >> a deep neural network are doing is notoriously difficult; that said much >> progress is being made). So we want to build explainable, end-to-end >> trained systems, and to accomplish this we ourselves need to understand = how >> these algorithms work, but in training and in inference. >> >> This email is already TL;DR but I'll add one more here: We need to learn >> control, not just prediction. Since we live in an inherently adversarial >> environment we need to take advantage of Reinforcement Learning as well = as >> the various attacks being formulated against ML; [5] gives one interesti= ng >> example of attacks against policy networks using adversarial examples. S= ee >> also slides 31 and 32 of [6] for some more on this topic. >> >> I hope some of this gets us thinking about the problems we need to solve >> in order to be successful in the ML space. There's plenty more of this o= n >> http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. >> I'm looking forward to the discussion. >> >> Thanks, >> >> --dmm >> >> >> >> >> [0] http://packetpushers.net/podcast/podcasts/pq-show-107-a >> pplicability-machine-learning-networking/ >> >> [1] http://sebastianruder.com/transfer-learning/index.html >> [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning= / >> [3] https://openai.com/blog/ >> [4] http://distill.pub/ >> [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf >> [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> > --001a113773b4b03668054be36485 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hey=C2=A0Jo=C3=A3o,

Thanks for the references. More to read and understand! = I'll just pint out here that=C2=A0``Mathematics and the Internet: A Source of Enormous Confusion and Great= Potential'' was written by two of my good friends and colleagues (= John and Walter). See http://www.1-4-5.net/~dmm/ml/talks/2017/nanog61.pptx for so= me of the work John and I have done. There's lots more of this stuff on= http://www.1-4-5.net/~dmm/v= ita.hml under Recent Talks.

Small world!<= /span>


Dave


On W= ed, Mar 29, 2017 at 11:53 AM, Jo=C3=A3o Paulo S. Medeiros <jpsm1985@gmail= .com> wrote:
Dear David Meyer and all,

I would like to contribut= e with some ideas and published works from my academic education.

I have been wor= king with Machine Learning (ML) and Computer Network since 2007. I initiall= y=C2=A0started my research interests with the use of neural networks to aid= the performance of classification and characterization of remote computer = fingerprinting (e.g. [0] [1], and most recently [2] [3]). Although it is no= t my current main line of research, my experience will agree with the David= 's comment that ``we need to think abo= ut is publicly available standardized data''. This probably = is one of the main problems for researchers trying to advance or reproduce = state-of-the-art research on Intrusion Detection systems (and others featur= e extraction + pattern recognition tasks) using ML.

My current main line of=C2=A0= research is related to two of David's concerns: namely, (i) the=C2=A0UTON and the (ii) Controllability of Compute= r Networks. My PhD thesis work was related to the use of model which could = be used to minimize the overhead of network monitoring. My last published w= ork about this is in [4]. I used the theory of Complex Networks Controllabi= lity [5] to achieve my PhD goal.=C2=A0Howeve= r, I realized that its too more practical to use this theory to build Obser= vable (dual problem) network monitoring systems with minimal sensor nodes, = since in controllability we need to directly change (or induce) the state o= f network devices. In this sense, the theory of Adaptive Filtering (e.g. Ka= lman Filter) is important too. Even so, Controlability of computer networks= it's still a very interesting and challenging=C2=A0problem involving n= ot only Complex Networks theory, but also, probably, Markov Process and ML.=

=
Still about UTON, the network topology alm= ost always plays an important role in the ML system design. For many reason= s, the topology is not available and its estimation is also another importa= nt problem we could approach using ML [6].
=
Finally, I would like to share an insp= iring paper entitled ``Mathematics and the Internet: A Source of Enormous C= onfusion and Great Potential'' [7].

Best regards!

[0]=C2=A0http://dx.doi.org/10.= 1109/EFTA.2007.4416854
[1]=C2=A0= http://dx.doi.org/10.1007/978-3-540-89173-4_20
[2]=C2=A0http://dx.doi.org/10.1007/978-3-319-05885-6_12=
[3]=C2=A0http://dx.doi.org/10.1201/b17333-= 10
[4]=C2=A0http://dx.doi.org/= 10.1109/CIT/IUCC/DASC/PICOM.2015.15
[5] h= ttp://dx.doi.org/10.1038/nature10011
[6] http://dx.doi.org/10.1109/TNET.2011.2175747
[7]=C2=A0http://www.ams.org/notices/200905/tx09= 0500586p.pdf

-- Prof. Jo=C3=A3o Paulo Souza Medeiros
<= /div>

On Wed, Mar 22, 2017 = at 2:29 PM, David Meyer <dmm@1-4-5.net> wrote:
Folks,

I thought I'd try to = get some discussion going by outlining some of my views as to why networkin= g is lagging other areas in the development and application of Machine Lear= ning (ML). In particular, networking is way behind what we might call the &= quot;perceptual tasks" (vision, NLP, robotics, etc) as well as other a= reas (medicine, finance, ...). The attached slide from one of my decks trie= s to summarize the situation, but I'll give a bit of an outline below.= =C2=A0

So why is networking lagging many other fields when it c= omes to the application of machine learning? There are several reasons whic= h I'll try to outline here (I was fortunate enough to discuss this with= the packetpushers crew a few weeks ago, see [0]). These are in no particul= ar order.

First, we don't have a "useful" theory = of networking (UTON). One way to think about what such a theory would look = like is by analogy to what we see with the success of convolutional neural = networks (CNNs) not only for vision but now for many other tasks. In that c= ase there is a theory of how vision works, built up from concepts like rece= ptive fields, shared weights, simple and complex cells, etc. For example, t= he input layer of a CNN isn't fully connected; rather connections refle= ct the receptive field of the input layer, which is in a way that is "= inspired" by biological vision (being very careful with "biologic= al inspiration"). Same with the alternation of convolutional and pooli= ng layers; these loosely model the alternation of simple and complex cells = in the primary visual cortex (V1), the secondary visual cortex(V2) and the = Brodmann area (V3).=C2=A0BTW, such a theory seems to be required for transf= er learning [1], which we'll need if we don't want every network to= be analyzed in an ad-hoc, one-off style (like we see today).
<= div>
T= he second thing that we need to think about is publicly available standardi= zed data sets. Examples here include MNIST, ImageNet, and many others. The = result of having these data sets has been the steady=C2=A0ratcheting down o= f error rates on tasks such as object and scene recognition, NLP, and other= s to super-human levels. Suffice it to say we have nothing like these data = sets for networking. Networking data sets today are largely proprietary, an= d because there is no UTON, there is no real way to compare results between= them.

Third, there is a large skill set gap. Network engineers= (us!) typically don't have the mathematical background required to bui= ld effective machine learning at scale. See [2] for an outline of some of t= he mathematical skills that are essential for effective ML. There is a lot = more to this, involving how progress is made in ML (open data, open source,= open models, in general open science and associated communities, see e.g.,= OpenAi [3], Distill [4], and many others). In any event we need build comm= unity and gain new skills if we want to be able to develop and apply state = of the art machine learning algorithms to network data, at scale. The botto= m line is that it will be difficult if not impossible to be effective in th= e ML space if we ourselves don't understand how it works and further, i= f we can build explainable systems (noting that explaining what the individ= ual neurons in a deep neural network are doing is notoriously difficult; th= at said much progress is being made). So we want to build explainable, end-= to-end trained systems, and to accomplish this we ourselves need to underst= and how these algorithms work, but in training and in inference.

This email is already TL;DR but I'll add one more here: We need to le= arn control, not just prediction. Since we live in an inherently adversaria= l environment we need to take advantage of Reinforcement Learning as well a= s the various attacks being formulated against ML; [5] gives one interestin= g example of attacks against policy networks using adversarial examples. Se= e also slides 31 and 32 of [6] for some more on this topic.

I h= ope some of this gets us thinking about the problems we need to solve in or= der to be successful in the ML space. There's plenty more of this on http://www.1-4-5.n= et/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html.
_____________________________________= __________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet



--001a113773b4b03668054be36485-- From nobody Wed Mar 29 15:24:24 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 46B7D126C23 for ; Wed, 29 Mar 2017 15:24:23 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.9 X-Spam-Level: X-Spam-Status: No, score=-1.9 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 2MhrBoEY1gK3 for ; Wed, 29 Mar 2017 15:24:21 -0700 (PDT) Received: from dns.ing.unife.it (dns.ing.unife.it [192.167.215.104]) by ietfa.amsl.com (Postfix) with ESMTP id ECF0B1250B8 for ; Wed, 29 Mar 2017 15:24:20 -0700 (PDT) Received: from dns.endif.man (dns.endif.man [10.16.1.0]) by dns.ing.unife.it (Postfix) with ESMTP id 41B3970C49D; Thu, 30 Mar 2017 00:24:19 +0200 (CEST) Date: Thu, 30 Mar 2017 00:24:19 +0200 (CEST) From: Michele Zorzi X-X-Sender: zorzi@dns.endif.man To: Sheng Jiang cc: "idnet@ietf.org" In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD16656@NKGEML515-MBS.china.huawei.com> Message-ID: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com>, <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> <5D36713D8A4E7348A7E10DF7437A4B927CD16656@NKGEML515-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII; format=flowed Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 22:24:23 -0000 Hi all, as I think I mentioned some time back, there is an interesting venue for the type of work this community would like to promote, please check www.comsoc.org/tccn Please note that, although "cognitive communications and networking" may immediately ring the "cognitive radio" bell and thereby raise negative feelings in people not interested in PHY and radio, our interpretation tries to be much broader, and in fact we are trying to promote a vision in which cognition is applied at all layers of the protocol stack and across the network, with machine learning techniques and software defined networking as two key ingredients. As the EiC of this journal, I see it as a great opportunity if this community wants to start promoting good research in this area and I am willing to support it, e.g., through publishing (after rigorous peer-review of course) a good position paper/research roadmap coming from networking people. Best regards and good luck with your meeting, Michele From nobody Wed Mar 29 17:02:53 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id F3578128616 for ; Wed, 29 Mar 2017 17:02:50 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.199 X-Spam-Level: X-Spam-Status: No, score=-4.199 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id h6cOtMGc_szP for ; Wed, 29 Mar 2017 17:02:48 -0700 (PDT) Received: from gpo2.cc.swin.edu.au (gpo2.cc.swin.edu.au [136.186.1.31]) (using TLSv1 with cipher DHE-RSA-AES256-SHA (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id B1387126DEE for ; Wed, 29 Mar 2017 17:02:47 -0700 (PDT) Received: from [136.186.229.37] (garmitage.caia.swin.edu.au [136.186.229.37]) by gpo2.cc.swin.edu.au (8.14.3/8.14.3) with ESMTP id v2U02guh027386 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES128-SHA bits=128 verify=NO); Thu, 30 Mar 2017 11:02:43 +1100 From: grenville armitage To: idnet@ietf.org Message-ID: <58DC4B22.6090307@swin.edu.au> Date: Thu, 30 Mar 2017 11:02:42 +1100 User-Agent: Mozilla/5.0 (X11; FreeBSD amd64; rv:38.0) Gecko/20100101 Thunderbird/38.7.1 MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="------------040908060607080004060600" Archived-At: Subject: [Idnet] Uses case for ML -- automating traffic prioritisation X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 00:02:51 -0000 This is a multi-part message in MIME format. --------------040908060607080004060600 Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: 7bit Apologies for some repetition here -- I sent an earlier version of the below note to nmlrg@irtf.org in October last year, but then ran out of cycles to further engage in discussion. Just in case the idnet@ list has slightly expanded membership, here's a lightly-expanded version of the information again: Between ~2005 and ~2012 my group explored the application of machine learning to the task of classifying application flows. One of the specific use cases was to classify traffic that required different QoS treatment, and automate the subsequent configuration of bottleneck home gateways to achieve said QoS treatment. A more topical variant might be to use ML for recognising traffic you wish to de-prioritise (e.g. protection from DoS, enforcing scavenger-class service, etc) Our top-level DIFFUSE project page: http://caia.swin.edu.au/urp/diffuse/ A proof-of-concept implemented in OpenWRT: http://caia.swin.edu.au/urp/diffuse/openwrt/ (pre-cursor to DIFFUSE: http://caia.swin.edu.au/sitcrc/angel, and even earlier work on statistical traffic classification: http://caia.swin.edu.au/urp/dstc) Some past academic papers that might be of (historical) interest: Thuy T. T. Nguyen, Grenville Armitage, Philip Branch and Sebastian Zander. Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic IEEE/ACM Transactions on Networking, vol. 20 no. 6 pp. 1880-1894, December 2012 http://dx.doi.org/10.1109/TNET.2012.2187305 Thuy Nguyen and Grenville Armitage. A Survey of Techniques for Internet Traffic Classification using Machine Learning IEEE Communications Surveys & Tutorials, vol. 10 no. 4 pp. 56-76, 2008 http://dx.doi.org/10.1109/SURV.2008.080406 Jason But, Grenville Armitage and Lawrence Stewart. Outsourcing Automated QoS Control of Home Routers for a Better Online Game Experience IEEE Communications Magazine, vol. 46, no. 12, pp.64-70, December 2008 http://dx.doi.org/10.1109/MCOM.2008.4689209 cheers, gja --------------040908060607080004060600 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: 7bit
Apologies for some repetition here -- I sent an earlier version of the below note to nmlrg@irtf.org in October last year, but then ran out of cycles to further engage in discussion. Just in case the idnet@ list has slightly expanded membership, here's a lightly-expanded version of the information again:

Between ~2005 and ~2012 my group explored the application of machine learning to the task of classifying application flows. One of the specific use cases was to classify traffic that required different QoS treatment, and automate the subsequent configuration of bottleneck home gateways to achieve said QoS treatment. A more topical variant might be to use ML for recognising traffic you wish to de-prioritise (e.g. protection from DoS, enforcing scavenger-class service, etc)

Our top-level DIFFUSE project page: http://caia.swin.edu.au/urp/diffuse/
A proof-of-concept implemented in OpenWRT: http://caia.swin.edu.au/urp/diffuse/openwrt/
(pre-cursor to DIFFUSE: http://caia.swin.edu.au/sitcrc/angel, and even earlier work on statistical traffic classification: http://caia.swin.edu.au/urp/dstc)

Some past academic papers that might be of (historical) interest:

Thuy T. T. Nguyen, Grenville Armitage, Philip Branch and Sebastian Zander.
Timely and Continuous Machine-Learning-Based Classification for Interactive IP Traffic
IEEE/ACM Transactions on Networking, vol. 20 no. 6 pp. 1880-1894, December 2012
http://dx.doi.org/10.1109/TNET.2012.2187305

Thuy Nguyen and Grenville Armitage.
A Survey of Techniques for Internet Traffic Classification using Machine Learning
IEEE Communications Surveys & Tutorials, vol. 10 no. 4 pp. 56-76, 2008
http://dx.doi.org/10.1109/SURV.2008.080406

Jason But, Grenville Armitage and Lawrence Stewart.
Outsourcing Automated QoS Control of Home Routers for a Better Online Game Experience
IEEE Communications Magazine, vol. 46, no. 12, pp.64-70, December 2008
http://dx.doi.org/10.1109/MCOM.2008.4689209

cheers,
gja
--------------040908060607080004060600-- From nobody Wed Mar 29 17:09:32 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E031E124D68 for ; Wed, 29 Mar 2017 17:09:30 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.2 X-Spam-Level: X-Spam-Status: No, score=-4.2 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id S0_069zYIvGv for ; Wed, 29 Mar 2017 17:09:29 -0700 (PDT) Received: from gpo3.cc.swin.edu.au (gpo3.cc.swin.edu.au [136.186.1.32]) (using TLSv1 with cipher DHE-RSA-AES256-SHA (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 39206126D05 for ; Wed, 29 Mar 2017 17:09:28 -0700 (PDT) Received: from [136.186.229.37] (garmitage.caia.swin.edu.au [136.186.229.37]) by gpo3.cc.swin.edu.au (8.14.3/8.14.3) with ESMTP id v2U09FvI010405 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES128-SHA bits=128 verify=NO); Thu, 30 Mar 2017 11:09:15 +1100 To: "Liubing (Leo)" References: <5631DBFA.1090404@swin.edu.au> <8AE0F17B87264D4CAC7DE0AA6C406F45C23180F9@nkgeml506-mbx.china.huawei.com> Cc: idnet@ietf.org From: grenville armitage Message-ID: <58DC4CAB.7000506@swin.edu.au> Date: Thu, 30 Mar 2017 11:09:15 +1100 User-Agent: Mozilla/5.0 (X11; FreeBSD amd64; rv:38.0) Gecko/20100101 Thunderbird/38.7.1 MIME-Version: 1.0 In-Reply-To: <8AE0F17B87264D4CAC7DE0AA6C406F45C23180F9@nkgeml506-mbx.china.huawei.com> Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: 7bit Archived-At: Subject: Re: [Idnet] [Nmlrg] Uses case for ML -- automating traffic prioritisation X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 00:09:31 -0000 (Belatedly following up on a question I failed to answer on nmrlg@ last year... I figure idnet@ is the appropriate place for a mea culpa and followup...) On 10/30/2015 13:50, Liubing (Leo) wrote: > Hi Grenville, > > Thanks for sharing your use case. I think it is a valuable use case in networking. > > You mentioned the PoC: >> A proof-of-concept implemented in OpenWRT: >> http://caia.swin.edu.au/urp/diffuse/openwrt/ > Does it imply that your study was specific to home networks scenarios, and the algorithms were specifically designed to adapt those low performance embedded devices? Our specific demonstration use-case was home networking, yes. But I don't recall that we specifically made algorithm choices for low performance embedded devices. Of course, when a Professor says "my group" did something, it means the student(s) at the time were the ones who really understood the finer details :-) > I personally expect your study was not limited in home network scenarios, because the flow classification is also an important use case in carrier networks. Agreed. My particular focus was on simplifying the remote configuration of home gateways so they could automagically prioritise interactive traffic without being pre-loaded with N*thousands of static QoS rules for all possible VoIP, game, etc, services. But the idea generalises. cheers, gja From nobody Wed Mar 29 17:18:26 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 5D6981201F8 for ; Wed, 29 Mar 2017 17:18:24 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.199 X-Spam-Level: X-Spam-Status: No, score=-4.199 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id t1dVireSaujS for ; Wed, 29 Mar 2017 17:18:22 -0700 (PDT) Received: from gpo3.cc.swin.edu.au (gpo3.cc.swin.edu.au [136.186.1.32]) (using TLSv1 with cipher DHE-RSA-AES256-SHA (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5662712947F for ; Wed, 29 Mar 2017 17:18:22 -0700 (PDT) Received: from [136.186.229.37] (garmitage.caia.swin.edu.au [136.186.229.37]) by gpo3.cc.swin.edu.au (8.14.3/8.14.3) with ESMTP id v2U0IJrs012702 (version=TLSv1/SSLv3 cipher=DHE-RSA-AES128-SHA bits=128 verify=NO); Thu, 30 Mar 2017 11:18:20 +1100 To: idnet@ietf.org References: From: grenville armitage Message-ID: <58DC4ECB.60809@swin.edu.au> Date: Thu, 30 Mar 2017 11:18:19 +1100 User-Agent: Mozilla/5.0 (X11; FreeBSD amd64; rv:38.0) Gecko/20100101 Thunderbird/38.7.1 MIME-Version: 1.0 In-Reply-To: Content-Type: multipart/alternative; boundary="------------030904050009020900030607" Archived-At: Subject: [Idnet] FPS game traffic datasets... Re: A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 00:18:24 -0000 This is a multi-part message in MIME format. --------------030904050009020900030607 Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: 7bit On 03/23/2017 04:29, David Meyer wrote: [...] > The second thing that we need to think about is publicly available standardized data sets. Examples here include MNIST, ImageNet, and many others. The result of having these data sets has been the steady ratcheting down of error rates on tasks such as object and scene recognition, NLP, and others to super-human levels. Suffice it to say we have nothing like these data sets for networking. Networking data sets today are largely proprietary, and because there is no UTON, there is no real way to compare results between them. Admittedly narrow in scope, and a bit dusty with age, but we have some early-2000s first person shooter (FPS) game traffic traces available at http://caia.swin.edu.au/sitcrc/song (Sadly, not much changed since 2006 when project funding ended. But perhaps still of interest to someone.) cheers, gja -- Professor Grenville Armitage School of Software and Electrical Engineering Faculty of Science, Engineering and Technology Swinburne University of Technology, Australia http://i4t.swin.edu.au/people/garmitage --------------030904050009020900030607 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable
On 03/23/2017 04:29, David Meyer wrote= :
=C2=A0=C2=A0=C2=A0 [...]
The second thing that we need to think about is publicly available standardized data sets. Examples here include MNIST, ImageNet, and many others. The result of having these data sets has been the steady=C2=A0ratcheting down of error rates on tasks such as object and scene recognition, NLP, and others to super-human levels. Suffice it to say we have nothing like these data sets for networking. Networking data sets today are largely proprietary, and because there is no UTON, there is no real way to compare results between them.

Admittedly narrow in scope, and a bit dusty with age, but we have some early-2000s first person shooter (FPS) game traffic traces available at http://caia.swin.edu.au/sitcrc/song=C2=A0=C2=A0= (Sadly, not much changed since 2006 when project funding ended. But perhaps still of interest to someone.)

cheers,
gja
--=20
Professor Grenville Armitage
School of Software and Electrical Engineering
Faculty of Science, Engineering and Technology
Swinburne University of Technology, Australia
http://i4t.swin.edu.au/people/garmitage
--------------030904050009020900030607-- From nobody Wed Mar 29 19:27:38 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 6C9AC1294E2 for ; Tue, 28 Mar 2017 19:13:26 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.698 X-Spam-Level: X-Spam-Status: No, score=-2.698 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id hLuKMiDkDblQ for ; Tue, 28 Mar 2017 19:13:24 -0700 (PDT) Received: from mail-oi0-x22a.google.com (mail-oi0-x22a.google.com [IPv6:2607:f8b0:4003:c06::22a]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 9B36C128954 for ; Tue, 28 Mar 2017 19:13:21 -0700 (PDT) Received: by mail-oi0-x22a.google.com with SMTP id f193so1203695oib.2 for ; Tue, 28 Mar 2017 19:13:21 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=Zwr+dI3fBtnlJk6Hhy6fsXr2BYKOrlqrmhj1/25ZXPs=; b=kQ+gE0W/FwaQ0xsO2lGWvrpPAXODR0fHQJOBrRsnkGxWyuIVoGuu3YsLfqfpebo+Op yhUkYoT25XqjB+lD5J7pzbViXrurXjfwAX2/rL/ravSL6IFixtPljvJw7DZ0bUW4gLpA 7AAxG7gKcI6palnQIAY2J6fLU6O9FHPsv9x1913tpIGRjMUoAzrIs0QLGaPva5JMA+J5 HH/JC2uTh8idH9mrM+yNKrB40DlV/gCHPlF9ghpPWS+dOYaYc+W8m2WPpbhRWMMVshtl +U6b9ar5zYlbV/Tgtte6wg/zL5p8gDZY3kLKK3K0JRYapd+ZItfBd8dT/lY+wEBiQo6Z RLwg== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=Zwr+dI3fBtnlJk6Hhy6fsXr2BYKOrlqrmhj1/25ZXPs=; b=dY5+qtGhonV0MAZspIt06TbX+tOVQpuXUK3VJ8GTKJqjdJ90HlsrZbjhkNV4w+uFEC VdXzF7C9Sj/q41oohaHXZ1aJi6UxiHoiAe0poJN8p9jm0FgZ8+3tJnhMGsrUOVBgpEbU GBtoAxi1GnzNT6dhfJp+IsHaTnJfjlySJQU7UsLg+g6Jz+OhApx0+LvyQzqCgx+9Zbie oy6rjru0q+E2LIRwRNwoNt8khDg9pGeN25uKpVh+Q1hcsi+A4gseY+c2K9ABB+KxZS5T HkTnmezP2QN71JyqQQP0Jkn0MxGhOd9ThGv9BSAJvPh8jiKHUkg2AUJ7VTmR5C51gPTD ytdQ== X-Gm-Message-State: AFeK/H2/ot+f3+eRa+RVy4K6ZHrIEJwXiLqjfVWatnadOKF3ZoiO+ybKH5S+/kF6Fls9j5dNJk9TFgUx84u5SQ== X-Received: by 10.202.51.10 with SMTP id z10mr16879221oiz.195.1490753600929; Tue, 28 Mar 2017 19:13:20 -0700 (PDT) MIME-Version: 1.0 Received: by 10.157.43.164 with HTTP; Tue, 28 Mar 2017 19:13:20 -0700 (PDT) In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: Manav Bhatia Date: Wed, 29 Mar 2017 07:43:20 +0530 Message-ID: To: Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a113cdfe8d4c6a1054bd51e4c Archived-At: X-Mailman-Approved-At: Wed, 29 Mar 2017 19:27:37 -0700 Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Wed, 29 Mar 2017 02:13:26 -0000 --001a113cdfe8d4c6a1054bd51e4c Content-Type: text/plain; charset=UTF-8 Hi, I am looking at machine learning for predictive analytics. We are building an SDN controller that has access to large volumes of network data and events. We can look at traffic shifts over a period of time and look for underlying patterns in that and use that to predict how the network services could be provisioned. Do we have any use case document that describes how we could apply machine learning/advanced analytics principles in the networking domain? Cheers, Manav On Tue, Mar 28, 2017 at 9:59 PM, Sheng Jiang wrote: > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, we > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 > https://portal.etsi.org/webapp/WorkProgram/Report_ > WorkItem.asp?WKI_ID=51011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a113cdfe8d4c6a1054bd51e4c Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hi,

I am looking at machine learning fo= r predictive analytics. We are building an SDN controller that has access t= o large volumes of network data and events. We can look at traffic shifts o= ver a period of time and look for underlying patterns in that and use that = to predict how the network services could be provisioned.

Do we have any use case document that describes how we could apply = machine learning/advanced analytics principles in the networking domain?

Cheers, Manav
=
On Tue, Mar 28, 2017 at 9:59 PM, Sheng Jiang= <jiangsheng@huawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com .= Then we may have an informal meeting to discuss some common interests and = potential future activities (not any activities in IETF, but also other STO= or experimental trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--001a113cdfe8d4c6a1054bd51e4c-- From nobody Thu Mar 30 07:41:57 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id A8B8E126C26 for ; Thu, 30 Mar 2017 07:41:56 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.221 X-Spam-Level: X-Spam-Status: No, score=-4.221 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id S16E7myFpVbI for ; Thu, 30 Mar 2017 07:41:53 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id A09BF1294FB for ; Thu, 30 Mar 2017 07:41:50 -0700 (PDT) Received: from 172.18.7.190 (EHLO LHREML710-CAH.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DJZ07343; Thu, 30 Mar 2017 14:41:48 +0000 (GMT) Received: from NKGEML414-HUB.china.huawei.com (10.98.56.75) by LHREML710-CAH.china.huawei.com (10.201.108.33) with Microsoft SMTP Server (TLS) id 14.3.301.0; Thu, 30 Mar 2017 15:41:47 +0100 Received: from NKGEML515-MBS.china.huawei.com ([169.254.5.223]) by nkgeml414-hub.china.huawei.com ([10.98.56.75]) with mapi id 14.03.0235.001; Thu, 30 Mar 2017 22:41:40 +0800 From: Sheng Jiang To: David Meyer CC: "idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGqBEgAgAAAmQCAA20JNA== Date: Thu, 30 Mar 2017 14:41:40 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CD16EE8@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> , In-Reply-To: Accept-Language: en-GB, zh-CN, en-US Content-Language: en-GB X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.148.199] Content-Type: multipart/alternative; boundary="_000_5D36713D8A4E7348A7E10DF7437A4B927CD16EE8NKGEML515MBSchi_" MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A090203.58DD192D.002F, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.5.223, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: b03537fe13089659dc877e9f8409ba26 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 14:41:57 -0000 --_000_5D36713D8A4E7348A7E10DF7437A4B927CD16EE8NKGEML515MBSchi_ Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Hi, David, I think I agree with you, but in slight different expression. Yes, the har= d parts of getting ML into Network lies on machine learning. But, it is not= that we need to develop any new ML technical/algorithms for networking in = particular. It is that we MUST re-set up our network domain knowledge from = the perspective of applying ML. My slides [0] does not suggest that *someon= e else* will handle the ML part. Actually, oppositely, it suggests some exp= erts who have knowledge of both ML and network (probably we) would develop = tools/algorithms/systems to handle the ML part for other network experts (m= ore than 98 percent of current network administrators). So that, these netw= ork experts would be allowed to manage their network easily with intelligen= ce association, but no need to become ML experts themselves. Here, we would= like to treat the network administrators like the users in other successfu= l ML application. We are the domian experts to do the dirty AI work for the= m. I believe we have common understanding in the above description. But certai= nly my slides needs further refine to clarify my viewpoint. Best regards, Sheng ________________________________ From: IDNET [idnet-bounces@ietf.org] on behalf of David Meyer [dmm@1-4-5.ne= t] Sent: 29 March 2017 2:01 To: Sheng Jiang Cc: idnet@ietf.org Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for= Interests s/NMRL/NMLRG/ (sorry about that). Dave On Tue, Mar 28, 2017 at 10:59 AM, David Meyer > wrote: Hey Sheng, I just wanted to revive my key concern on [0] (same one I made at the NMRL)= : The hard parts of getting Machine Learning intelligence into Networking i= s the Machine Learning part. In addition, successful deployment of ML requi= res knowledge of ML combined with domain knowledge. We definitely have the = domain knowledge; the problem is that we don't have the ML knowledge, and t= his is one of the big factors holding us back; see e.g. Andrew's discussion= of talent in [1]. Slides such as [0] seem to imply that *someone else* (i= n particular, not us) will handle the ML part of all of this. I'll just no= te that in general successful deployments of ML don't work this way; the do= main experts will have to learn ML (and vice versa) for us to be successful= (again, see [1] and many others). Perhaps a useful exercise would be to write an ID that makes your assumptio= ns explicit? Thanks, Dave [0] https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf [1] https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-ri= ght-now On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang > wrote: Hi, all, Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9. https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-def= ined-network-01.pdf Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may ha= ve an informal meeting to discuss some common interests and potential futur= e activities (not any activities in IETF, but also other STO or experimenta= l trails, etc.) on Thursday morning. FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below. https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D510= 11 Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area. Best regards, Sheng _______________________________________________ IDNET mailing list IDNET@ietf.org https://www.ietf.org/mailman/listinfo/idnet --_000_5D36713D8A4E7348A7E10DF7437A4B927CD16EE8NKGEML515MBSchi_ Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable
Hi, David,

I think I agree with you, but in slight different  expression. Ye= s, the hard parts of getting ML into Network lies on machine learning. But,= it is not that we need to develop any new ML technical/algorithms for netw= orking in particular. It is that we MUST re-set up our network domain knowledgeboth ML and n= etwork (probably we) would develop tools/algorithms/systems to handle the M= L part for other network experts (more than 98 percent of current network administrators). So that, these network experts would b= e allowed to manage their network easily with intelligence association, but= no need to become ML experts themselves. Here, we would like to treat the = network administrators like the users in other successful ML application. We are the domian experts to do = the dirty AI work for them.

I believe we have common understanding in the above description. But c= ertainly my slides needs further refine to clarify my viewpoint.

Best regards,

Sheng
From: IDNET [idnet-bounces@ietf.org] on b= ehalf of David Meyer [dmm@1-4-5.net]
Sent: 29 March 2017 2:01
To: Sheng Jiang
Cc: idnet@ietf.org
Subject: Re: [Idnet] Intelligence-Defined Network Architecture and C= all for Interests

s/NMRL/NMLRG/   (sorry about that). Dave

On Tue, Mar 28, 2017 at 10:59 AM, David Meyer <dmm@1-4-5.net>= ; wrote:
Hey Sheng,

I just wanted to revive my key concern on [0] (same one I made at the = NMRL): The hard parts of getting Machine Learning intelligence into Network= ing is the Machine Learning part. In addition, successful deployment of ML = requires knowledge of ML combined with domain knowledge. We definitely have the domain knowledge; the proble= m is that we don't have the ML knowledge, and this is one of the big factor= s holding us back; see e.g. Andrew's discussion of talent in [1].  Sli= des such as [0] seem to imply that *someone else* (in particular, not us)  will handle the ML part of all of this= . I'll just note that in general successful deployments of ML don't work th= is way; the domain experts will have to learn ML (and vice versa) for us to= be successful (again, see [1] and many others).

Perhaps a useful exercise would be to write an ID that makes your assu= mptions explicit?

Thanks,

Dave
 



On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang <jiangsheng@h= uawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in page 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawei.com . Then we may have an informal meeting to discuss= some common interests and potential future activities (not any activities = in IETF, but also other STO or experimental trails, etc.)  on Thursday= morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


--_000_5D36713D8A4E7348A7E10DF7437A4B927CD16EE8NKGEML515MBSchi_-- From nobody Thu Mar 30 07:43:36 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id E272B12950B for ; Thu, 30 Mar 2017 07:43:34 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -3.534 X-Spam-Level: X-Spam-Status: No, score=-3.534 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_MED=-2.3, SPF_SOFTFAIL=0.665] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id pr0xKbPC0wO6 for ; Thu, 30 Mar 2017 07:43:32 -0700 (PDT) Received: from mailgw1.uni-kl.de (mailgw1.uni-kl.de [IPv6:2001:638:208:120::220]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id C60501294FB for ; Thu, 30 Mar 2017 07:43:31 -0700 (PDT) Received: from dfki.uni-kl.de (dfki-1002.dfki.uni-kl.de [131.246.195.2]) by mailgw1.uni-kl.de (8.14.4/8.14.4/Debian-8+deb8u1) with ESMTP id v2UEhJAC010841; Thu, 30 Mar 2017 16:43:19 +0200 Received: from serv-4100.kl.dfki.de (serv-4100.kl.dfki.de [192.168.41.180]) by dfki.uni-kl.de (8.13.8+Sun/8.11.4) with ESMTP id v2UEhIuk012369; Thu, 30 Mar 2017 16:43:19 +0200 (CEST) Received: from DESKTOPOK6R5TK (gast-5316.kl.dfki.de [192.168.53.216]) by serv-4100.kl.dfki.de (8.14.4+Sun/8.14.4) with ESMTP id v2UEhIEl006099; Thu, 30 Mar 2017 16:43:18 +0200 (CEST) From: "Wei Jiang" To: "'Manav Bhatia'" , "'Sheng Jiang'" Cc: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: Date: Thu, 30 Mar 2017 16:43:00 +0200 Message-ID: <005e01d2a963$f0d4a590$d27df0b0$@dfki.de> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="----=_NextPart_000_005F_01D2A974.B45FE690" X-Mailer: Microsoft Outlook 15.0 Thread-Index: AQHvuMMK5A4jH2eeCIK6TGFbk+vGpwJ9RVFpoV9pj2A= Content-Language: en-us Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 14:43:35 -0000 This is a multipart message in MIME format. ------=_NextPart_000_005F_01D2A974.B45FE690 Content-Type: text/plain; charset="UTF-8" Content-Transfer-Encoding: quoted-printable Hi Manav, =20 I am working on SELFNET project (one of the nineteen H2020 5G-PPP Phase = 1 projects: https://5g-ppp.eu/5g-ppp-phase-1-projects/ ), which was = driven by the motivation of applying Machine Learning to autonomically = manage the SDN/NFV-based 5G networks (https://selfnet-5g.eu/ ). Three Use Cases, i.e., Self-Healing, Self-Protection, and = Self-Optimization, are exploring, and have been specified in D2.2 = (https://bscw.selfnet-5g.eu/pub/bscw.cgi/d18751-4/*/*/*/*/DOI-D2.1.html = ). =20 Don=E2=80=99t know whether we can find some synergies. =20 My 2 cents. =20 Best regards, Wei Jiang =20 From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Manav Bhatia Sent: Wednesday, March 29, 2017 4:13 AM To: Sheng Jiang Cc: idnet@ietf.org Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call = for Interests =20 Hi, =20 I am looking at machine learning for predictive analytics. We are = building an SDN controller that has access to large volumes of network = data and events. We can look at traffic shifts over a period of time and = look for underlying patterns in that and use that to predict how the = network services could be provisioned. =20 Do we have any use case document that describes how we could apply = machine learning/advanced analytics principles in the networking domain? =20 Cheers, Manav =20 On Tue, Mar 28, 2017 at 9:59 PM, Sheng Jiang > wrote: Hi, all, Although there are many understanding for Intelligence-Defined Network, = we are actually using this IDN as a term reference to the SDN-beyond = architecture that we presented in IETF97, see the below link. A = reference model is presented in page 3, while potential standardization = works is presented in page 9. https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-d= efined-network-01.pdf Although it might be a little bit too early for AI/ML in network giving = the recent story of the concluded proposed NMLRG, we still would like to = call for interests in IDN. Anybody (on site in Chicago this week) are = interested in this or even wider topics regarding to AI/ML in network, = please contact me on jiangsheng@huawei.com = . Then we may have an informal meeting = to discuss some common interests and potential future activities (not = any activities in IETF, but also other STO or experimental trails, etc.) = on Thursday morning. FYI, we have already working on a Work Item, called IDN in the ETSI NGP = (Next Generation Protocol) ISG, links below. https://portal.etsi.org/tb.aspx?tbid=3D844 = &SubTB=3D844 https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D5= 1011 Meanwhile, please do use this mail list as a forum to discuss any topics = that may applying AI/ML into network area. Best regards, Sheng _______________________________________________ IDNET mailing list IDNET@ietf.org =20 https://www.ietf.org/mailman/listinfo/idnet =20 ------=_NextPart_000_005F_01D2A974.B45FE690 Content-Type: text/html; charset="UTF-8" Content-Transfer-Encoding: quoted-printable

Hi Manav,

 

I am working on SELFNET project (one of the nineteen H2020 5G-PPP = Phase 1 projects: https://5g-ppp.eu/5g-= ppp-phase-1-projects/ ), which was driven by the motivation of = applying Machine Learning to autonomically manage the SDN/NFV-based 5G = networks (https://selfnet-5g.eu/ = ).

Three Use Cases, i.e., Self-Healing, Self-Protection, and = Self-Optimization, are exploring, and have been specified in D2.2 (https://bscw.selfnet-5g.eu/pub/bscw.cgi/d18751-4/*/*/*/*/DOI-D2.1.= html ).

 

Don=E2=80=99t know whether we can find some = synergies.

 

My 2 cents.

 

Best regards,

Wei Jiang

 

From: IDNET = [mailto:idnet-bounces@ietf.org] On Behalf Of Manav = Bhatia
Sent: Wednesday, March 29, 2017 4:13 AM
To: = Sheng Jiang
Cc: idnet@ietf.org
Subject: Re: [Idnet] = Intelligence-Defined Network Architecture and Call for = Interests

 

Hi,

 

I = am looking at machine learning for predictive analytics. We are building = an SDN controller that has access to large volumes of network data and = events. We can look at traffic shifts over a period of time and look for = underlying patterns in that and use that to predict how the network = services could be provisioned.

 

Do we have any use case document that describes how we = could apply machine learning/advanced analytics principles in the = networking domain?

 

Cheers, Manav

 

On Tue, = Mar 28, 2017 at 9:59 PM, Sheng Jiang <jiangsheng@huawei.com> = wrote:

Hi, = all,

Although there are many understanding for = Intelligence-Defined Network, we are actually using this IDN as a term = reference to the SDN-beyond architecture that we presented in IETF97, = see the below link. A reference model is presented in page 3, while = potential standardization works is presented in page 9.

https://www.ietf.org/proceedings/97/slides/slides-97-nm= lrg-intelligence-defined-network-01.pdf

Although it might be = a little bit too early for AI/ML in network giving the recent story of = the concluded proposed NMLRG, we still would like to call for interests = in IDN. Anybody (on site in Chicago this week) are interested in this or = even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawei.com . Then = we may have an informal meeting to discuss some common interests and = potential future activities (not any activities in IETF, but also other = STO or experimental trails, etc.)  on Thursday morning.

FYI, = we have already working on a Work Item, called IDN in the ETSI NGP (Next = Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D= 844
https://portal.etsi.org/webapp/WorkProgram/Report_WorkI= tem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail = list as a forum to discuss any topics that may applying AI/ML into = network area.

Best = regards,

Sheng
_______________________________________________<= br>IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

 

------=_NextPart_000_005F_01D2A974.B45FE690-- From nobody Thu Mar 30 08:36:58 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 53125128990 for ; Thu, 30 Mar 2017 08:36:57 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2 X-Spam-Level: X-Spam-Status: No, score=-2 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 2qRl2Pp6XPDs for ; Thu, 30 Mar 2017 08:36:54 -0700 (PDT) Received: from mail-it0-x243.google.com (mail-it0-x243.google.com [IPv6:2607:f8b0:4001:c0b::243]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id BA5D7127449 for ; Thu, 30 Mar 2017 08:36:54 -0700 (PDT) Received: by mail-it0-x243.google.com with SMTP id 190so13375588itm.3 for ; Thu, 30 Mar 2017 08:36:54 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=subject:to:references:cc:from:organization:message-id:date :user-agent:mime-version:in-reply-to:content-transfer-encoding; bh=cgICkkFPh+nv+iNMlw89d+BexMYUeeL7XN1pqkCiRRs=; b=poFpK6MAbDNwX7DbRcIj58ZuQZhJoApCMBjqGr/GIxLIKtivSHzUkuoEmfInHy1QJy fh7vwhqcdM8yzmxgOwYApVBwkcA8zOYy3l4S3L2Y0MVTuL5v8ZDvUceJ/xwnUvqvAmmy ErfBdufY3ln6GMj2HnjHYPai4NsQHDIcDN6nuMKngH4WiSPHTbA7pBrCPRT38wHHDbNb NXSoTUe0L9n5spSg2Y+9K56d+WONlMpZTViWCGaSHAJbozPoMpxyRgP/Y7OxvrKCe0e3 G49Ymu2b5XGMeRpGjdbaS7/KFZu3D/yiyl8FCGyE39fvoWu1RTXGfCmUrMnR05BXiVPl CFlA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:subject:to:references:cc:from:organization :message-id:date:user-agent:mime-version:in-reply-to :content-transfer-encoding; bh=cgICkkFPh+nv+iNMlw89d+BexMYUeeL7XN1pqkCiRRs=; b=cGdTUM+LNIhTSO6NEpKLTzCZt+OfHJTYq8jYenxLV+fCDngqLAoK4n/aOu/3Y8SGWW OwZWprYHU65ZeTY4JrW6BD84A76PSlOR70u+1u4Fw5WqEuwAiohhqeaITGI8xq0vXJIb T4aGe8xwsK7NmvUKBwVckKN1cuw/BZS8L6JHXTPT/t6YZbEnu62Jm3rFbTb+LYXzN3hd W9w/UukArPEzDtMimEzpRZ/c+YWlGwVE7Bnog1cGUZXM4QtOaMNJ9ZqnDTKKEtTLEoU4 UXgTDHhYhoGe8C1rU3fh/WmQA8WxelpKnedXlK8hLpUECpsVaH8Db/LXFLgqpb4PGVrN ZS/g== X-Gm-Message-State: AFeK/H0vVmboxEbtwWngJ3ClM8OD1DJLKU9VG/keVajxaCwRa2zUfgGZOG51zxha5biU6Q== X-Received: by 10.36.122.135 with SMTP id a129mr5103260itc.16.1490888214027; Thu, 30 Mar 2017 08:36:54 -0700 (PDT) Received: from ?IPv6:2001:67c:370:128:28cc:dc4c:9703:6781? 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[2001:67c:370:128:28cc:dc4c:9703:6781]) by smtp.gmail.com with ESMTPSA id e195sm5169534itb.18.2017.03.30.08.36.52 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Thu, 30 Mar 2017 08:36:53 -0700 (PDT) To: Sheng Jiang , David Meyer References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5D36713D8A4E7348A7E10DF7437A4B927CD16EE8@NKGEML515-MBS.china.huawei.com> Cc: "idnet@ietf.org" From: Brian E Carpenter Organization: University of Auckland Message-ID: Date: Fri, 31 Mar 2017 04:37:00 +1300 User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD16EE8@NKGEML515-MBS.china.huawei.com> Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 15:36:57 -0000 Agreed, and there are (still) two key points: 1. What is our underlying model (what Dave called a "theory of networking")? With no such model, it's very hard to tell the ML system what to do. 2. And as others have said: get hold of large datasets that can processed by ML according to that model. For developing open solutions, a corpus of open data sets seems essential. As anybody from the network measurement community will tell you, getting hold of large data sets from operators is extremely difficult for both privacy and commercial reasons. Brian On 31/03/2017 03:41, Sheng Jiang wrote: > Hi, David, > > I think I agree with you, but in slight different expression. Yes, the hard parts of getting ML into Network lies on machine learning. But, it is not that we need to develop any new ML technical/algorithms for networking in particular. It is that we MUST re-set up our network domain knowledge from the perspective of applying ML. My slides [0] does not suggest that *someone else* will handle the ML part. Actually, oppositely, it suggests some experts who have knowledge of both ML and network (probably we) would develop tools/algorithms/systems to handle the ML part for other network experts (more than 98 percent of current network administrators). So that, these network experts would be allowed to manage their network easily with intelligence association, but no need to become ML experts themselves. Here, we would like to treat the network administrators like the users in other successful ML application. We are the domian experts to do the dirty AI work for them. > > I believe we have common understanding in the above description. But certainly my slides needs further refine to clarify my viewpoint. > > Best regards, > > Sheng > ________________________________ > From: IDNET [idnet-bounces@ietf.org] on behalf of David Meyer [dmm@1-4-5.net] > Sent: 29 March 2017 2:01 > To: Sheng Jiang > Cc: idnet@ietf.org > Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests > > s/NMRL/NMLRG/ (sorry about that). Dave > > On Tue, Mar 28, 2017 at 10:59 AM, David Meyer > wrote: > Hey Sheng, > > I just wanted to revive my key concern on [0] (same one I made at the NMRL): The hard parts of getting Machine Learning intelligence into Networking is the Machine Learning part. In addition, successful deployment of ML requires knowledge of ML combined with domain knowledge. We definitely have the domain knowledge; the problem is that we don't have the ML knowledge, and this is one of the big factors holding us back; see e.g. Andrew's discussion of talent in [1]. Slides such as [0] seem to imply that *someone else* (in particular, not us) will handle the ML part of all of this. I'll just note that in general successful deployments of ML don't work this way; the domain experts will have to learn ML (and vice versa) for us to be successful (again, see [1] and many others). > > Perhaps a useful exercise would be to write an ID that makes your assumptions explicit? > > Thanks, > > Dave > > > [0] https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-defined-network-01.pdf > [1] https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now > > > On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang > wrote: > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, we are actually using this IDN as a term reference to the SDN-beyond architecture that we presented in IETF97, see the below link. A reference model is presented in page 3, while potential standardization works is presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving the recent story of the concluded proposed NMLRG, we still would like to call for interests in IDN. Anybody (on site in Chicago this week) are interested in this or even wider topics regarding to AI/ML in network, please contact me on jiangsheng@huawei.com . Then we may have an informal meeting to discuss some common interests and potential future activities (not any activities in IETF, but also other STO or experimental trails, etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=51011 > > Meanwhile, please do use this mail list as a forum to discuss any topics that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > From aydinulas@gmail.com Thu Mar 30 09:23:04 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 683231297E1 for ; Thu, 30 Mar 2017 09:23:04 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.701 X-Spam-Level: X-Spam-Status: No, score=-1.701 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, FREEMAIL_FORGED_FROMDOMAIN=0.197, FREEMAIL_FROM=0.001, HEADER_FROM_DIFFERENT_DOMAINS=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=no autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id rqOrI2AY513R for ; Thu, 30 Mar 2017 09:23:00 -0700 (PDT) Received: from mail-pg0-x22c.google.com (mail-pg0-x22c.google.com [IPv6:2607:f8b0:400e:c05::22c]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 1B0EB129865 for ; Thu, 30 Mar 2017 09:22:59 -0700 (PDT) Received: by mail-pg0-x22c.google.com with SMTP id 21so44603442pgg.1 for ; Thu, 30 Mar 2017 09:22:59 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:sender:in-reply-to:references:from:date:message-id :subject:to:cc; bh=E7rq1Gn7whDLgGsdQ0oTq7YmIBWvj80BrQzWVHW1vnY=; b=LDFvh8DNDR1zlyeN9sSYhB7yRfa6/wMCmG6exhhIZEtPsv933G3srFlGB0LhsHM7rh jPrHsrFgHKy1w20FigNMduha3/pfhHPRU3RM8i3BHxAuM4/5iikdaq2CV4hLmpONmAvf 97e6y1eD+3Y25XCP4HjGl4QtjWw6CfbiD5MUEdI9wAVIuahLXRr0wT6WPYOtNW3UR6yC LajxGXuJ1nZF/Ff5c6h4SY3s1+cFCTfd4cMct+QiTWuiwp18dAAqbyZ0vCQdCJBHSZ6I PzK5BhnvmSUHkLdBR14/o8PgcQqcjBE8GX579PbFdIQehTzw9jS5DuiPkLiLKizXvLrt KeBA== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:sender:in-reply-to:references:from :date:message-id:subject:to:cc; bh=E7rq1Gn7whDLgGsdQ0oTq7YmIBWvj80BrQzWVHW1vnY=; b=fLhomoG+VKMZx/xNDviuZo6wXo0D7wjQNpeqOIhB5uh7Wzztv7SdS8zEXMYWTU+B00 M2OmJrREdoEkGCRg8MB85MOh3VMr5fdrD0glx1FUUhrCWtqprkVSQIw0LHilJ9me9POk b76EL5Y78J0kgtFRflbCNWDM58vamWwoEzJZevnwYQrgDzdCsFOMgJL4xwRb4kqHQKRf C6FZK48xwqPrQIiKAywEAHnPsGF2QydJ/iPC+UykJm3iM4WIlMU6JI9MhVL9Tzt5dcS6 7pbV5n4Z/vD384AU/jdms7tlSaPLoHgleNHS/hrqJDogcfY+IGhaL4pI5YLrJfGo7bwM qTyA== X-Gm-Message-State: AFeK/H3zNOoO5snyfzp6uwffUa62DyYKcYrT8JPeBpS1aJDyMotLCcjC5CrZ+m5CXGiPrz2DN64BMYoUEFMEEQ== X-Received: by 10.84.138.193 with SMTP id 59mr578772plp.167.1490890978613; Thu, 30 Mar 2017 09:22:58 -0700 (PDT) MIME-Version: 1.0 Sender: aydinulas@gmail.com Received: by 10.100.138.74 with HTTP; Thu, 30 Mar 2017 09:22:18 -0700 (PDT) In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5D36713D8A4E7348A7E10DF7437A4B927CD16EE8@NKGEML515-MBS.china.huawei.com> From: Aydin Ulas Date: Thu, 30 Mar 2017 19:22:18 +0300 X-Google-Sender-Auth: j0UNLhNGiMOzzzd6Td8UhrY3EXM Message-ID: To: Brian E Carpenter Cc: Sheng Jiang , David Meyer , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c1450e22ddf2a054bf51b70 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 30 Mar 2017 16:30:55 -0000 --94eb2c1450e22ddf2a054bf51b70 Content-Type: text/plain; charset=UTF-8 Dear all, As a researcher having almost 20 years of experience in machine learning and working as a solution architect on SDN and NFV for the past four years, I would like to offer my two cents. I believe that I have the unique perspective to look at the problem from the other angle and this mailing list is the perfectly suited ground for it. The problem (or the solution as David suggested) is the lack of standardized data sets and also the problems defined over those data sets. In the machine learning area, we had this luxury almost since the beginning (see ML repository: https://archive.ics.uci.edu/ml/about.html); it makes the job relatively easy and also comparable since researchers are working on the same problem, trying to do better, and also exchanging information. Of course, with the rise of the Internet, smart phone revolution and the huge amount of data produced in the last decade, combined with privacy concerns, things have not been like they were before (Netflix reverse engineering scandal did not help either) but people are still donating their data for researchers to use because the community is *more research,* *less industry* oriented. Apart from medical data (where doctors are more conservative to give you and privacy has a stronger impact), the trend continues to this day and makes it easier for the likes of us. Instead, in the networking community, as far as I have seen in the past five years, usually the operators own the data and they are reluctant (also privacy is an important aspect) to release this data (even an obfuscated version of it) even for their own needs. I have been trying to detect a protocol using packets for the past two weeks and the operator which requires this feature does not provide us the data to work on. To draw researchers from Machine Learning area what should be done in my opinion is to provide well-defined problems with data supporting them and the rest will definitely come in rapid succession. For the data, of course supervised learning is the most established and easier to apply technique (so labels are important) but you do not always need labels (at least all of them) to apply semi-supervised and non-supervised learning algorithms. Most algorithms are fault tolerant, fault being missing features, missing labels, etc. There are techniques which support multiple instances, dissimilarities between objects that I have yet to see in the networking domain, which could bring unique perspectives to problems. I am trying to persuade my colleagues who have ML experience to work on networking problems but it is really hard to provide them the problem and the data which could get the ball rolling. Best regards, Aydin Ulas, PhD, Argela A.S., Bogazici University, MEF University https://www.cmpe.boun.edu.tr/~ulas/ On Thu, Mar 30, 2017 at 6:37 PM, Brian E Carpenter < brian.e.carpenter@gmail.com> wrote: > Agreed, and there are (still) two key points: > > 1. What is our underlying model (what Dave called a "theory of > networking")? With no such model, it's very hard to tell the ML system what > to do. > > 2. And as others have said: get hold of large datasets that can processed > by ML according to that model. For developing open solutions, a corpus of > open data sets seems essential. As anybody from the network measurement > community will tell you, getting hold of large data sets from operators is > extremely difficult for both privacy and commercial reasons. > > Brian > > > On 31/03/2017 03:41, Sheng Jiang wrote: > > Hi, David, > > > > I think I agree with you, but in slight different expression. Yes, the > hard parts of getting ML into Network lies on machine learning. But, it is > not that we need to develop any new ML technical/algorithms for networking > in particular. It is that we MUST re-set up our network domain knowledge > from the perspective of applying ML. My slides [0] does not suggest that > *someone else* will handle the ML part. Actually, oppositely, it suggests > some experts who have knowledge of both ML and network (probably we) would > develop tools/algorithms/systems to handle the ML part for other network > experts (more than 98 percent of current network administrators). So that, > these network experts would be allowed to manage their network easily with > intelligence association, but no need to become ML experts themselves. > Here, we would like to treat the network administrators like the users in > other successful ML application. We are the domian experts to do the dirty > AI work for them. > > > > I believe we have common understanding in the above description. But > certainly my slides needs further refine to clarify my viewpoint. > > > > Best regards, > > > > Sheng > > ________________________________ > > From: IDNET [idnet-bounces@ietf.org] on behalf of David Meyer [ > dmm@1-4-5.net] > > Sent: 29 March 2017 2:01 > > To: Sheng Jiang > > Cc: idnet@ietf.org > > Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call > for Interests > > > > s/NMRL/NMLRG/ (sorry about that). Dave > > > > On Tue, Mar 28, 2017 at 10:59 AM, David Meyer dmm@1-4-5.net>> wrote: > > Hey Sheng, > > > > I just wanted to revive my key concern on [0] (same one I made at the > NMRL): The hard parts of getting Machine Learning intelligence into > Networking is the Machine Learning part. In addition, successful deployment > of ML requires knowledge of ML combined with domain knowledge. We > definitely have the domain knowledge; the problem is that we don't have the > ML knowledge, and this is one of the big factors holding us back; see e.g. > Andrew's discussion of talent in [1]. Slides such as [0] seem to imply > that *someone else* (in particular, not us) will handle the ML part of all > of this. I'll just note that in general successful deployments of ML don't > work this way; the domain experts will have to learn ML (and vice versa) > for us to be successful (again, see [1] and many others). > > > > Perhaps a useful exercise would be to write an ID that makes your > assumptions explicit? > > > > Thanks, > > > > Dave > > > > > > [0] https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > [1] https://hbr.org/2016/11/what-artificial-intelligence-can- > and-cant-do-right-now > > > > > > On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang > wrote: > > Hi, all, > > > > Although there are many understanding for Intelligence-Defined Network, > we are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > > > https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com > . Then we may have an informal meeting to discuss some common interests and > potential future activities (not any activities in IETF, but also other STO > or experimental trails, etc.) on Thursday morning. > > > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > > > https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 > > https://portal.etsi.org/webapp/WorkProgram/Report_ > WorkItem.asp?WKI_ID=51011 > > > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > > > Best regards, > > > > Sheng > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > > > > > > > > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --94eb2c1450e22ddf2a054bf51b70 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Dear all,
As a researcher having almost 20 years of ex= perience in machine learning and working as a solution architect on SDN and= NFV for the past four years, I would like to offer my two cents.
I believe that I have the unique perspective to look at the problem from t= he other angle and this mailing list is the perfectly suited ground for it.= The problem (or the solution as David suggested) is the lack of standardiz= ed data sets and also the problems defined over those data sets. In the mac= hine learning area, we had this luxury almost since the beginning (see ML r= epository: https://ar= chive.ics.uci.edu/ml/about.html); it makes the job relatively easy and = also comparable since researchers are working on the same problem, trying t= o do better, and also exchanging information. Of course, with the rise of t= he Internet, smart phone revolution and the huge amount of data produced in= the last decade, combined with privacy concerns, things have not been like= they were before (Netflix reverse engineering scandal did not help either)= but people are still=C2=A0donating their data for researchers to use becau= se the community is more research, less industry oriented. Apart from medical data (where doctors are more conservative to= give you and privacy has a stronger impact), the trend continues to this d= ay and makes it easier for the likes of us. Instead, in the networking comm= unity, as far as I have seen in the past five years, usually the operators = own the data and they are reluctant (also privacy is an important aspect) t= o release this data (even an obfuscated version of it) even for their own n= eeds. I have been trying to detect a protocol using packets for the past tw= o weeks and the operator which requires this feature does not provide us th= e data to work on.

To draw researchers from Machin= e Learning area what should be done in my opinion is to provide well-define= d problems with data supporting them and the rest will definitely come in r= apid succession. For the data, of course supervised learning is the most es= tablished and easier to apply technique (so labels are important) but you d= o not always need labels (at least all of them) to apply semi-supervised an= d non-supervised learning algorithms. Most algorithms are fault tolerant, f= ault being missing features, missing labels, etc. There are techniques whic= h support multiple instances, dissimilarities between objects that I have y= et to see in the networking domain, which could bring unique perspectives t= o problems. I am trying to persuade my colleagues who have ML experience to= work on networking problems but it is really hard to provide them the prob= lem and the data which could get the ball rolling.


Best regards,
Aydin Ulas, PhD,
Argela A= .S., Bogazici University, MEF University
<= br>


On Thu, Mar 30, 2017 at 6:37 PM, Brian E Carpenter <brian.e.carpenter@gmail.com> wrote:
Agreed, and there are (still) two key points:

1. What is our underlying model (what Dave called a "theory of network= ing")? With no such model, it's very hard to tell the ML system wh= at to do.

2. And as others have said: get hold of large datasets that can processed b= y ML according to that model. For developing open solutions, a corpus of op= en data sets seems essential. As anybody from the network measurement commu= nity will tell you, getting hold of large data sets from operators is extre= mely difficult for both privacy and commercial reasons.

=C2=A0 =C2=A0Brian


On 31/03/2017 03:41, Sheng Jiang wrote:
> Hi, David,
>
> I think I agree with you, but in slight different=C2=A0 expression. Ye= s, the hard parts of getting ML into Network lies on machine learning. But,= it is not that we need to develop any new ML technical/algorithms for netw= orking in particular. It is that we MUST re-set up our network domain knowl= edge from the perspective of applying ML. My slides [0] does not suggest th= at *someone else* will handle the ML part. Actually, oppositely, it suggest= s some experts who have knowledge of both ML and network (probably we) woul= d develop tools/algorithms/systems to handle the ML part for other network = experts (more than 98 percent of current network administrators). So that, = these network experts would be allowed to manage their network easily with = intelligence association, but no need to become ML experts themselves. Here= , we would like to treat the network administrators like the users in other= successful ML application. We are the domian experts to do the dirty AI wo= rk for them.
>
> I believe we have common understanding in the above description. But c= ertainly my slides needs further refine to clarify my viewpoint.
>
> Best regards,
>
> Sheng
> ________________________________
> From: IDNET [idnet-bounces@i= etf.org] on behalf of David Meyer [dmm= @1-4-5.net]
> Sent: 29 March 2017 2:01
> To: Sheng Jiang
> Cc: idnet@ietf.org
> Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Cal= l for Interests
>
> s/NMRL/NMLRG/=C2=A0 =C2=A0(sorry about that). Dave
>
> On Tue, Mar 28, 2017 at 10:59 AM, David Meyer = <dmm@1-4-5.net<mailto:dmm@1-4-5.net>> wrote:
> Hey Sheng,
>
> I just wanted to revive my key concern on [0] (same one I made at the = NMRL): The hard parts of getting Machine Learning intelligence into Network= ing is the Machine Learning part. In addition, successful deployment of ML = requires knowledge of ML combined with domain knowledge. We definitely have= the domain knowledge; the problem is that we don't have the ML knowled= ge, and this is one of the big factors holding us back; see e.g. Andrew'= ;s discussion of talent in [1].=C2=A0 Slides such as [0] seem to imply that= *someone else* (in particular, not us)=C2=A0 will handle the ML part of al= l of this. I'll just note that in general successful deployments of ML = don't work this way; the domain experts will have to learn ML (and vice= versa) for us to be successful (again, see [1] and many others).
>
> Perhaps a useful exercise would be to write an ID that makes your assu= mptions explicit?
>
> Thanks,
>
> Dave
>
>
> [0] https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-int= elligence-defined-network-01.pdf
> [1] https://hbr.o= rg/2016/11/what-artificial-intelligence-can-and-cant-do-right-now=
>
>
> On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang &= lt;jiangsheng@huawei.com<ma= ilto:jiangsheng@huawei.com>> wrote:
> Hi, all,
>
> Although there are many understanding for Intelligence-Defined Network= , we are actually using this IDN as a term reference to the SDN-beyond arch= itecture that we presented in IETF97, see the below link. A reference model= is presented in page 3, while potential standardization works is presented= in page 9.
>
> h= ttps://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelli= gence-defined-network-01.pdf
>
> Although it might be a little bit too early for AI/ML in networ= k giving the recent story of the concluded proposed NMLRG, we still would l= ike to call for interests in IDN. Anybody (on site in Chicago this week) ar= e interested in this or even wider topics regarding to AI/ML in network, pl= ease contact me on jiangsheng@huaw= ei.com<mailto:jiangshe= ng@huawei.com> . Then we may have an informal meeting to discuss som= e common interests and potential future activities (not any activities in I= ETF, but also other STO or experimental trails, etc.)=C2=A0 on Thursday mor= ning.
>
> FYI, we have already working on a Work Item, called IDN in the ETSI NG= P (Next Generation Protocol) ISG, links below.
>
> https://portal.etsi.org/tb.aspx?= tbid=3D844&SubTB=3D844
> https://portal.ets= i.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011 >
> Meanwhile, please do use this mail list as a forum to discuss any topi= cs that may applying AI/ML into network area.
>
> Best regards,
>
> Sheng
> _______________________________________________
> IDNET mailing list
> IDNET@ietf.org<mailto:= IDNET@ietf.org>
> https://www.ietf.org/mailman/listinfo/idnet=
>
>
>
>
>
> _______________________________________________
> IDNET mailing list
> IDNET@ietf.org
> https://www.ietf.org/mailman/listinfo/idnet=
>

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ietfa.amsl.com (Postfix) with ESMTPS id 8B4EB129646 for ; Thu, 30 Mar 2017 18:04:34 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml708-cah.china.huawei.com) ([172.18.7.190]) by lhrrg02-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DDV96727; Fri, 31 Mar 2017 01:04:32 +0000 (GMT) Received: from NKGEML412-HUB.china.huawei.com (10.98.56.73) by lhreml708-cah.china.huawei.com (10.201.108.49) with Microsoft SMTP Server (TLS) id 14.3.301.0; Fri, 31 Mar 2017 02:04:30 +0100 Received: from DGGEMM403-HUB.china.huawei.com (10.3.20.211) by nkgeml412-hub.china.huawei.com (10.98.56.73) with Microsoft SMTP Server (TLS) id 14.3.235.1; Fri, 31 Mar 2017 09:04:25 +0800 Received: from DGGEMM506-MBS.china.huawei.com ([169.254.4.150]) by DGGEMM403-HUB.china.huawei.com ([10.3.20.211]) with mapi id 14.03.0301.000; Fri, 31 Mar 2017 09:04:18 +0800 From: "dingxiaojian (A)" To: Brian E Carpenter , Sheng Jiang , David Meyer CC: "idnet@ietf.org" Thread-Topic: [Idnet] Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AdKpuq/NwtlzKdpeR7GeyjmOWS/6lA== Date: Fri, 31 Mar 2017 01:04:17 +0000 Message-ID: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> Accept-Language: zh-CN, en-US Content-Language: zh-CN X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.134.134.227] Content-Type: text/plain; charset="gb2312" Content-Transfer-Encoding: base64 MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020204.58DDAB20.017D, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.4.150, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 52cf5905a98df339d3dfef0b405ea5f2 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: 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Oi8vcG9ydGFsLmV0c2kub3JnL3dlYmFwcC9Xb3JrUHJvZ3JhbS9SZXBvcnRfV29ya0l0ZW0uYXNw P1dLSV9JRD0NCj4gNTEwMTENCj4gDQo+IE1lYW53aGlsZSwgcGxlYXNlIGRvIHVzZSB0aGlzIG1h aWwgbGlzdCBhcyBhIGZvcnVtIHRvIGRpc2N1c3MgYW55IHRvcGljcyB0aGF0IG1heSBhcHBseWlu ZyBBSS9NTCBpbnRvIG5ldHdvcmsgYXJlYS4NCj4gDQo+IEJlc3QgcmVnYXJkcywNCj4gDQo+IFNo ZW5nDQo+IF9fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fDQo+ IElETkVUIG1haWxpbmcgbGlzdA0KPiBJRE5FVEBpZXRmLm9yZzxtYWlsdG86SURORVRAaWV0Zi5v cmc+DQo+IGh0dHBzOi8vd3d3LmlldGYub3JnL21haWxtYW4vbGlzdGluZm8vaWRuZXQNCj4gDQo+ IA0KPiANCj4gDQo+IA0KPiBfX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19f X19fX19fXw0KPiBJRE5FVCBtYWlsaW5nIGxpc3QNCj4gSURORVRAaWV0Zi5vcmcNCj4gaHR0cHM6 Ly93d3cuaWV0Zi5vcmcvbWFpbG1hbi9saXN0aW5mby9pZG5ldA0KPiANCg0KX19fX19fX19fX19f X19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX19fX18NCklETkVUIG1haWxpbmcgbGlzdA0K SURORVRAaWV0Zi5vcmcNCmh0dHBzOi8vd3d3LmlldGYub3JnL21haWxtYW4vbGlzdGluZm8vaWRu ZXQNCg== From nobody Thu Mar 30 20:07:29 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 178AE129717 for ; Thu, 30 Mar 2017 20:07:28 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.901 X-Spam-Level: X-Spam-Status: No, score=-1.901 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id jWfGtuRAMntB for ; Thu, 30 Mar 2017 20:07:26 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 0DB31129704 for ; Thu, 30 Mar 2017 20:07:25 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v2V37PZ7003155 for ; Fri, 31 Mar 2017 12:07:25 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw2.nict.go.jp with ESMTP id v2V37PJx003151 for ; Fri, 31 Mar 2017 12:07:25 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id C60E2A9F3 for ; Fri, 31 Mar 2017 12:07:24 +0900 (JST) Date: Fri, 31 Mar 2017 12:07:20 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170331030720.GF4808@spectre> References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 03:07:28 -0000 Hi all, That's it and I insist that, if anybody in the mailing list is bound to an operator or a similar organization that manages huge amounts of data, just the data we need, please try to provide it. We will be able to help you to build some anonymization solution, if needed. Thank you. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Thu Mar 30 21:12:41 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B9C0A129771 for ; Thu, 30 Mar 2017 21:12:39 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.696 X-Spam-Level: X-Spam-Status: No, score=-4.696 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, RCVD_IN_MSPIKE_H2=-2.796, SPF_HELO_PASS=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); 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charset="utf-8"; format=flowed Content-Transfer-Encoding: 7bit Hello, A couple of suggestions: -Why not raise this point / request in various WGs (e.g. in the OPS Area) and RGs (e.g. MAPRG, NMRG). -Why not work with the IETF meeting NOC to collect data sets? BR, Laurent. On 31/03/2017 05:07, Pedro Martinez-Julia wrote: > Hi all, > > That's it and I insist that, if anybody in the mailing list is bound to > an operator or a similar organization that manages huge amounts of data, > just the data we need, please try to provide it. We will be able to help > you to build some anonymization solution, if needed. Thank you. > > Regards, > Pedro > -- Laurent Ciavaglia Nokia, Bell Labs +33 160 402 636 route de Villejust - Nozay, France linkedin.com/in/laurent.ciavaglia --------------09FB2E0EAD139491FBD81F32 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: 8bit Hello,

A couple of suggestions:
    -Why not raise this point / request in various WGs (e.g. in the OPS Area) and RGs (e.g. MAPRG, NMRG).
    -Why not work with the IETF meeting NOC to collect data sets?

BR, Laurent.

On 31/03/2017 05:07, Pedro Martinez-Julia wrote:
Hi all,

That's it and I insist that, if anybody in the mailing list is bound to
an operator or a similar organization that manages huge amounts of data,
just the data we need, please try to provide it. We will be able to help
you to build some anonymization solution, if needed. Thank you.

Regards,
Pedro

--

Laurent Ciavaglia

Nokia, Bell Labs

 

+33 160 402 636

route de Villejust - Nozay, France

linkedin.com/in/laurent.ciavaglia

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charset="utf-8"; format=flowed Content-Transfer-Encoding: 7bit Also CAIDA has multiple measurement tools to collect data. Some IETFers are specialists of these tools and report findings on data collected via these tools. Might be something to look at. Also, and with this I'm finished for today, documenting the characteristics of the requested data set(s) might help. BR, Laurent. On 31/03/2017 06:12, Laurent Ciavaglia wrote: > Hello, > > A couple of suggestions: > -Why not raise this point / request in various WGs (e.g. in the > OPS Area) and RGs (e.g. MAPRG, NMRG). > -Why not work with the IETF meeting NOC to collect data sets? > > BR, Laurent. > > On 31/03/2017 05:07, Pedro Martinez-Julia wrote: >> Hi all, >> >> That's it and I insist that, if anybody in the mailing list is bound to >> an operator or a similar organization that manages huge amounts of data, >> just the data we need, please try to provide it. We will be able to help >> you to build some anonymization solution, if needed. Thank you. >> >> Regards, >> Pedro >> > > -- > > Laurent Ciavaglia > > Nokia, Bell Labs > > +33 160 402 636 > > route de Villejust - Nozay, France > > linkedin.com/in/laurent.ciavaglia > -- Laurent Ciavaglia Nokia, Bell Labs +33 160 402 636 route de Villejust - Nozay, France linkedin.com/in/laurent.ciavaglia --------------7F1007795A84E10CAF1F9040 Content-Type: text/html; charset="utf-8" Content-Transfer-Encoding: 8bit Also CAIDA has multiple measurement tools to collect data.
Some IETFers are specialists of these tools and report findings on data collected via these tools.

Might be something to look at.

Also, and with this I'm finished for today, documenting the characteristics of the requested data set(s) might help.

BR, Laurent.

On 31/03/2017 06:12, Laurent Ciavaglia wrote:
Hello,

A couple of suggestions:
    -Why not raise this point / request in various WGs (e.g. in the OPS Area) and RGs (e.g. MAPRG, NMRG).
    -Why not work with the IETF meeting NOC to collect data sets?

BR, Laurent.

On 31/03/2017 05:07, Pedro Martinez-Julia wrote:
Hi all,

That's it and I insist that, if anybody in the mailing list is bound to
an operator or a similar organization that manages huge amounts of data,
just the data we need, please try to provide it. We will be able to help
you to build some anonymization solution, if needed. Thank you.

Regards,
Pedro

--

Laurent Ciavaglia

Nokia, Bell Labs

 

+33 160 402 636

route de Villejust - Nozay, France

linkedin.com/in/laurent.ciavaglia


--

Laurent Ciavaglia

Nokia, Bell Labs

 

+33 160 402 636

route de Villejust - Nozay, France

linkedin.com/in/laurent.ciavaglia

--------------7F1007795A84E10CAF1F9040-- From nobody Thu Mar 30 21:57:40 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 3EF83129546 for ; Thu, 30 Mar 2017 21:57:37 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -6.899 X-Spam-Level: X-Spam-Status: No, score=-6.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_HI=-5, RP_MATCHES_RCVD=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id UC_yH4kGckBi for ; Thu, 30 Mar 2017 21:57:34 -0700 (PDT) Received: from mail2-relais-roc.national.inria.fr (mail2-relais-roc.national.inria.fr [192.134.164.83]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5FA2112970F for ; Thu, 30 Mar 2017 21:57:33 -0700 (PDT) X-IronPort-AV: E=Sophos;i="5.36,250,1486422000"; d="scan'208,217";a="266955511" Received: from unknown (HELO [172.16.4.191]) ([38.69.5.76]) by mail2-relais-roc.national.inria.fr with ESMTP/TLS/DHE-RSA-AES128-SHA; 31 Mar 2017 06:57:31 +0200 To: idnet@ietf.org References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> <0e924c06-b8fb-48fa-5137-f4de120b03db@nokia-bell-labs.com> From: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= Message-ID: Date: Fri, 31 Mar 2017 06:57:29 +0200 User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <0e924c06-b8fb-48fa-5137-f4de120b03db@nokia-bell-labs.com> Content-Type: multipart/alternative; boundary="------------CFBF76BC70DEDAA5FED98F7F" Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 04:57:37 -0000 This is a multi-part message in MIME format. --------------CFBF76BC70DEDAA5FED98F7F Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Hi, Data is primordial but sharing them is very difficult (I'm not an operator) due to privacy but also legislative issues. Here are few comments: 1) on anonymization: this far from being simple and I do not believe that anybody can guarentee that nothing will be leaked out as sensitive data (and at the end this will the responsability of the operator who have shared the data). -> That is why sharing but not in public manner (with a NDA) is still the most viable solution (in my opinion) 2) charaterizing the data we would like to access is clearly a first step (even if many of us like to have raw data) 3) Another possibility is to run the algorithms you want to test on a remote platform without accessing the data and get back the results (also here there might be some safguards to avoid that program tries to extract sensitive data as results !) jerome Le 31/03/2017 =E0 06:15, Laurent Ciavaglia a =E9crit : > Also CAIDA has multiple measurement tools to collect data. > Some IETFers are specialists of these tools and report findings on > data collected via these tools. > > Might be something to look at. > > Also, and with this I'm finished for today, documenting the > characteristics of the requested data set(s) might help. > > BR, Laurent. > > On 31/03/2017 06:12, Laurent Ciavaglia wrote: >> Hello, >> >> A couple of suggestions: >> -Why not raise this point / request in various WGs (e.g. in the >> OPS Area) and RGs (e.g. MAPRG, NMRG). >> -Why not work with the IETF meeting NOC to collect data sets? >> >> BR, Laurent. >> >> On 31/03/2017 05:07, Pedro Martinez-Julia wrote: >>> Hi all, >>> >>> That's it and I insist that, if anybody in the mailing list is bound = to >>> an operator or a similar organization that manages huge amounts of da= ta, >>> just the data we need, please try to provide it. We will be able to h= elp >>> you to build some anonymization solution, if needed. Thank you. >>> >>> Regards, >>> Pedro >>> >> >> --=20 >> >> Laurent Ciavaglia >> >> Nokia, Bell Labs >> >> =20 >> >> +33 160 402 636 >> >> route de Villejust - Nozay, France >> >> linkedin.com/in/laurent.ciavaglia >> > > --=20 > > Laurent Ciavaglia > > Nokia, Bell Labs > > =20 > > +33 160 402 636 > > route de Villejust - Nozay, France > > linkedin.com/in/laurent.ciavaglia > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet --------------CFBF76BC70DEDAA5FED98F7F Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: 8bit Hi,

Data is primordial but sharing them is very difficult (I'm not an operator) due to privacy but also legislative issues.
Here are few comments:
1) on anonymization: this far from being simple and I do not believe that anybody can guarentee that nothing will be leaked out as sensitive data (and at the end this will the responsability of the operator who have shared the data).
-> That is why sharing but not in public manner (with a NDA) is still the most viable solution (in my opinion)

2) charaterizing the data we would like to access is clearly a first step (even if many of us like to have raw data)

3) Another possibility is to run the algorithms you want to test on a remote platform without accessing the data and get back the results (also here there might be some safguards to avoid that program tries to extract sensitive data as results !)

jerome

Le 31/03/2017 à 06:15, Laurent Ciavaglia a écrit :
Also CAIDA has multiple measurement tools to collect data.
Some IETFers are specialists of these tools and report findings on data collected via these tools.

Might be something to look at.

Also, and with this I'm finished for today, documenting the characteristics of the requested data set(s) might help.

BR, Laurent.

On 31/03/2017 06:12, Laurent Ciavaglia wrote:
Hello,

A couple of suggestions:
    -Why not raise this point / request in various WGs (e.g. in the OPS Area) and RGs (e.g. MAPRG, NMRG).
    -Why not work with the IETF meeting NOC to collect data sets?

BR, Laurent.

On 31/03/2017 05:07, Pedro Martinez-Julia wrote:
Hi all,

That's it and I insist that, if anybody in the mailing list is bound to
an operator or a similar organization that manages huge amounts of data,
just the data we need, please try to provide it. We will be able to help
you to build some anonymization solution, if needed. Thank you.

Regards,
Pedro

--

Laurent Ciavaglia

Nokia, Bell Labs

 

+33 160 402 636

route de Villejust - Nozay, France

linkedin.com/in/laurent.ciavaglia


--

Laurent Ciavaglia

Nokia, Bell Labs

 

+33 160 402 636

route de Villejust - Nozay, France

linkedin.com/in/laurent.ciavaglia



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--------------CFBF76BC70DEDAA5FED98F7F-- From nobody Fri Mar 31 01:12:45 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id ED247129504 for ; Fri, 31 Mar 2017 01:12:44 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -14.522 X-Spam-Level: X-Spam-Status: No, score=-14.522 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, RCVD_IN_DNSWL_HI=-5, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_HELO_PASS=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001, USER_IN_DEF_DKIM_WL=-7.5] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=cisco.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id L_AtszgSUED1 for ; Fri, 31 Mar 2017 01:12:42 -0700 (PDT) Received: from aer-iport-4.cisco.com (aer-iport-4.cisco.com [173.38.203.54]) (using TLSv1.2 with cipher DHE-RSA-SEED-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5544B129536 for ; Fri, 31 Mar 2017 01:12:42 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/simple; d=cisco.com; i=@cisco.com; l=3268; q=dns/txt; s=iport; t=1490947962; x=1492157562; h=subject:to:references:from:message-id:date:mime-version: in-reply-to:content-transfer-encoding; bh=opdoCGIl2MLikHrZcAlb/p9sLlBprcnioRL0+qU2B0Y=; b=mRMPADvb5zbiDGBXRFr5SHtle3FT90ybtn+8pbcjdI9IKY9x83YOJZCs GHB/zdA9tkLGIrgxxsOlvTpaLNHaNYxSLhaDwSm/l4Gi+wtjP5knupvB+ uRy/hSaKOHHpu583W16C+bRW24EuJVmIsa50PsucGMj6G3NOjyNDeIL8R 0=; X-IronPort-Anti-Spam-Filtered: true X-IronPort-Anti-Spam-Result: =?us-ascii?q?A0AGAQDrDt5Y/xbLJq1dGQEBAQEBAQEBA?= =?us-ascii?q?QEBBwEBAQEBg1RhgQuNdHOQY5VQgg4fC4JCgzYChAEYAQIBAQEBAQEBayiFFQE?= =?us-ascii?q?BAQECAQEBChEKCwE7GwsSBi4nIg4GARIbiWkIDq8NOopcAQEBAQEBAQMBAQEBA?= =?us-ascii?q?QEBAQEBHoZOggWCaoQchh0BBJBijAiGfYtTgXxUhFaKEZNtHziBBSUWCBgVQYZ?= =?us-ascii?q?cPTWHYYIuAQEB?= X-IronPort-AV: E=Sophos;i="5.36,251,1486425600"; d="scan'208";a="653651693" Received: from aer-iport-nat.cisco.com (HELO aer-core-1.cisco.com) ([173.38.203.22]) by aer-iport-4.cisco.com with ESMTP/TLS/DHE-RSA-AES256-GCM-SHA384; 31 Mar 2017 08:12:40 +0000 Received: from lojakab-mac.cisco.com (dhcp-10-61-100-82.cisco.com [10.61.100.82]) by aer-core-1.cisco.com (8.14.5/8.14.5) with ESMTP id v2V8Cdfx003429; Fri, 31 Mar 2017 08:12:39 GMT To: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , idnet@ietf.org References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> <0e924c06-b8fb-48fa-5137-f4de120b03db@nokia-bell-labs.com> From: Lori Jakab Organization: cisco Systems, Inc. Message-ID: <4cbf93ce-24a1-9f70-28e0-bf15f35b7e5b@cisco.com> Date: Fri, 31 Mar 2017 11:12:39 +0300 MIME-Version: 1.0 In-Reply-To: Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: 8bit Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 08:12:45 -0000 On 3/31/17 7:57 AM, Jérôme François wrote: > Hi, > > Data is primordial but sharing them is very difficult (I'm not an > operator) due to privacy but also legislative issues. > Here are few comments: > 1) on anonymization: this far from being simple and I do not believe > that anybody can guarentee that nothing will be leaked out as > sensitive data (and at the end this will the responsability of the > operator who have shared the data). > -> That is why sharing but not in public manner (with a NDA) is still > the most viable solution (in my opinion) > > 2) charaterizing the data we would like to access is clearly a first > step (even if many of us like to have raw data) > > 3) Another possibility is to run the algorithms you want to test on a > remote platform without accessing the data and get back the results > (also here there might be some safguards to avoid that program tries > to extract sensitive data as results !) I think 3) is probably the most viable way forward for now. Lack of public data sets is not a new challenge for networking research, and I doubt saying that we're going to use traffic traces for ML applications is going to change that. Creating an open source platform, either pure software or software + hardware (like RIPE Atlas probes) that runs the algorithms on the data sending back only the results may have a better chance of success. -Lori > > jerome > > Le 31/03/2017 à 06:15, Laurent Ciavaglia a écrit : >> Also CAIDA has multiple measurement tools to collect data. >> Some IETFers are specialists of these tools and report findings on >> data collected via these tools. >> >> Might be something to look at. >> >> Also, and with this I'm finished for today, documenting the >> characteristics of the requested data set(s) might help. >> >> BR, Laurent. >> >> On 31/03/2017 06:12, Laurent Ciavaglia wrote: >>> Hello, >>> >>> A couple of suggestions: >>> -Why not raise this point / request in various WGs (e.g. in the >>> OPS Area) and RGs (e.g. MAPRG, NMRG). >>> -Why not work with the IETF meeting NOC to collect data sets? >>> >>> BR, Laurent. >>> >>> On 31/03/2017 05:07, Pedro Martinez-Julia wrote: >>>> Hi all, >>>> >>>> That's it and I insist that, if anybody in the mailing list is bound to >>>> an operator or a similar organization that manages huge amounts of data, >>>> just the data we need, please try to provide it. We will be able to help >>>> you to build some anonymization solution, if needed. Thank you. >>>> >>>> Regards, >>>> Pedro >>>> >>> >>> -- >>> >>> Laurent Ciavaglia >>> >>> Nokia, Bell Labs >>> >>> >>> >>> +33 160 402 636 >>> >>> route de Villejust - Nozay, France >>> >>> linkedin.com/in/laurent.ciavaglia >>> >> >> -- >> >> Laurent Ciavaglia >> >> Nokia, Bell Labs >> >> >> >> +33 160 402 636 >> >> route de Villejust - Nozay, France >> >> linkedin.com/in/laurent.ciavaglia >> >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet From nobody Fri Mar 31 05:40:34 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 45702129487 for ; Fri, 31 Mar 2017 05:40:33 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id SzqUH5Wnylte for ; Fri, 31 Mar 2017 05:40:31 -0700 (PDT) Received: from mail-io0-x241.google.com (mail-io0-x241.google.com [IPv6:2607:f8b0:4001:c06::241]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 5FCC7129409 for ; Fri, 31 Mar 2017 05:40:31 -0700 (PDT) Received: by mail-io0-x241.google.com with SMTP id n76so5779271ioe.1 for ; Fri, 31 Mar 2017 05:40:31 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=subject:to:references:from:organization:message-id:date:user-agent :mime-version:in-reply-to:content-transfer-encoding; bh=QJOjUvb0bZ6bcNHv5R8t3zk4o97ehuHfOuxpskgGs4k=; b=X3EkADoxTgk0QueCONem7w6dNuFsZTpQ1ZJMrlwhECq6hnYbALU9mQBGdEQHicflty x9b3gkcILPlQZd/JhJGzTKMtHC9SO1nwVaSc4a9s8VNPiNLUcGLrR/TGxKKi9aaxi9f8 HFPqiAWKJIeHcjpDjB6jH5wyqox4s04rSptaY3zo7xnMpRIoQPDEmHjW6ZVk4g/59Hjd FnJSHrltOMc01nuV1PWwD/mDjwVlqRUR6QVNtGBz+jUHnnIOd8VNH9OaDlak1vJC6VS4 f1d1XjqCQ3+YmTtsCvM9F6qrF42Qre9BDL/FPWMxajqzK5t2/2mtojk5LF1fxzP9gDnb HCHQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:subject:to:references:from:organization :message-id:date:user-agent:mime-version:in-reply-to :content-transfer-encoding; bh=QJOjUvb0bZ6bcNHv5R8t3zk4o97ehuHfOuxpskgGs4k=; b=GEWuTeTAV6iwisze/mV9BQhngdp7My+fLggrGa5kdFIR1xwCo4L+IpZgz4MKD4WJj3 sRIjRHb+PvrEeVtojwoBx5Opv2TvExwBwTsoRlh9SH7QAujAO9RHj0l0aWf6+T137o2L rvfV61YbdhQQXz7BUgbE1J+sUVVMfJhoOV/DCrx/O7Vih3TT0p/qVkAuxhXYpoLL9bbU BKPKySkgCTY24zS9Bu5oAXm8n+4jG3Dxrwx7n4QNavcrCEw50XBiCWNmwBChbqCeswau WPLzvQ8a8droy0WolNpNV7THTQCInlgKhNxvfdNtsHudmheVcBwK1CeDTwOr+taEq3hJ 5MHg== X-Gm-Message-State: AFeK/H3LqPt4/rkZZhlYI3ZsbxKHFrqlaQsizQNAJxGp/ez1b4HCa8/FUaYDJjKJif91Yg== X-Received: by 10.107.28.73 with SMTP id c70mr3021529ioc.198.1490964030449; Fri, 31 Mar 2017 05:40:30 -0700 (PDT) Received: from [31.133.147.88] (dhcp-9358.meeting.ietf.org. [31.133.147.88]) by smtp.gmail.com with ESMTPSA id 71sm3064730iof.29.2017.03.31.05.40.29 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Fri, 31 Mar 2017 05:40:29 -0700 (PDT) To: Laurent Ciavaglia , Pedro Martinez-Julia , idnet@ietf.org References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> From: Brian E Carpenter Organization: University of Auckland Message-ID: Date: Sat, 1 Apr 2017 01:40:36 +1300 User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 12:40:33 -0000 I discussed this briefly yesterday with Al Morton (IETF BMWG co-chair, and very expert on traffic measurement). He confirms the problem. And of course the available datasets are all pcap format. Does that contain what we need? Regards Brian On 31/03/2017 17:12, Laurent Ciavaglia wrote: > Hello, > > A couple of suggestions: > -Why not raise this point / request in various WGs (e.g. in the OPS > Area) and RGs (e.g. MAPRG, NMRG). > -Why not work with the IETF meeting NOC to collect data sets? > > BR, Laurent. > > On 31/03/2017 05:07, Pedro Martinez-Julia wrote: >> Hi all, >> >> That's it and I insist that, if anybody in the mailing list is bound to >> an operator or a similar organization that manages huge amounts of data, >> just the data we need, please try to provide it. We will be able to help >> you to build some anonymization solution, if needed. Thank you. >> >> Regards, >> Pedro >> > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > From nobody Fri Mar 31 05:47:19 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 08412129664 for ; Fri, 31 Mar 2017 05:47:18 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id XkmbDx2TNcQt for ; Fri, 31 Mar 2017 05:47:16 -0700 (PDT) Received: from mail-it0-x244.google.com (mail-it0-x244.google.com [IPv6:2607:f8b0:4001:c0b::244]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id DED68129640 for ; Fri, 31 Mar 2017 05:47:15 -0700 (PDT) Received: by mail-it0-x244.google.com with SMTP id 190so1897527itm.3 for ; Fri, 31 Mar 2017 05:47:15 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=subject:to:references:from:organization:message-id:date:user-agent :mime-version:in-reply-to:content-transfer-encoding; bh=LaKO2AdNbK1pSYLifS38dAUwXk6JQWpceOAzUSpMf2c=; b=SzkZBOtDVTeOYmKY49+tnu6CKqlZY2tCBEXZjqrK1xU6mUZsY9Z9qApBR+hbMCMJkO UCPdOko8TUZgWp7WDRtEzwzFhojG8qoHwERwvaslaS0V34sAP3nwwQ8NvnEGOGiNPL2r zcPnTZsyl5QVCa7R8u5UM2/PKOVg6TRU6LsR9itgpXb1z51i7x8hPVC6wzVy7NMQzwr9 UHDnX9benbsch9UI+2QJhihXT9L2c1GgaVlhVWJXcjb0+wMLeaugBkQZIw0pFhJo+U1H qJ6EY1w8WPa4+wqPbyowD5Xos3M2uCp3TWHf33ar61SXCpt5tR+7Vx/PmO83C3s6Jdrb hr2w== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:subject:to:references:from:organization :message-id:date:user-agent:mime-version:in-reply-to :content-transfer-encoding; bh=LaKO2AdNbK1pSYLifS38dAUwXk6JQWpceOAzUSpMf2c=; b=XbvFtpLpsGG+FawbulHFlfllr/TxJzk6rgZU8sNpsiuDodnjz9oNwfCl+freYNOX7q 5CFCFn9Efj6+/1EDT+8pfS39Fpl/pornzc3LvzHrq3exbFauj2pChNmaoY+7nkKinRdy bSzWXysd9KItFcqISwPBAJbEk7LF1buTvoRqFx9oB3fYCwruq7maKrEe9JMqvoSfO5XE x8Imj1V/JZ4i3GOcpik4R2oxgJKPtyW1oQscs9yMmnPMgxdFee+j+MgNvripvjXQlYa6 HZzZbsvnu9MeNI1fruBLZMb5AmM27GnFehPElOVodxyIjjVNFH6AW5NF21a5uDxSbhbk 2cNA== X-Gm-Message-State: AFeK/H16hC4c8Xi/qGty9gzPBW/MqF86eIAGYQ25O0FAwqVd78NL4ACo91f6nDlP08fkTA== X-Received: by 10.36.66.81 with SMTP id i78mr3565552itb.15.1490964435116; Fri, 31 Mar 2017 05:47:15 -0700 (PDT) Received: from [31.133.147.88] (dhcp-9358.meeting.ietf.org. [31.133.147.88]) by smtp.gmail.com with ESMTPSA id g19sm3065762ioj.37.2017.03.31.05.47.14 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Fri, 31 Mar 2017 05:47:14 -0700 (PDT) To: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , idnet@ietf.org References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> <0e924c06-b8fb-48fa-5137-f4de120b03db@nokia-bell-labs.com> From: Brian E Carpenter Organization: University of Auckland Message-ID: <52152b22-0178-0531-c26d-bb15a5c53ec8@gmail.com> Date: Sat, 1 Apr 2017 01:47:21 +1300 User-Agent: Mozilla/5.0 (Windows NT 6.1; WOW64; rv:45.0) Gecko/20100101 Thunderbird/45.8.0 MIME-Version: 1.0 In-Reply-To: Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: quoted-printable Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 12:47:18 -0000 > 1) on anonymization: this far from being simple... Yes, and another issue is that it may even make the data useless. I hit that problem a few years ago when evaluating stateless hash algorithms fo= r use in the IPv6 flow label. The problem was that anonymization would chan= ge the addresses in a pcap trace in such a way that some regularity in the o= riginal data would be replaced by pseudo-random bits, which would completely fals= ify the results from a statistical viewpoint. I think that in the same way, M= L results from anonymized data might be meaningless - and there is no way t= o know that. Regards Brian On 31/03/2017 17:57, J=C3=A9r=C3=B4me Fran=C3=A7ois wrote: > Hi, >=20 > Data is primordial but sharing them is very difficult (I'm not an > operator) due to privacy but also legislative issues. > Here are few comments: > 1) on anonymization: this far from being simple and I do not believe > that anybody can guarentee that nothing will be leaked out as sensitive= > data (and at the end this will the responsability of the operator who > have shared the data). > -> That is why sharing but not in public manner (with a NDA) is still > the most viable solution (in my opinion) >=20 > 2) charaterizing the data we would like to access is clearly a first > step (even if many of us like to have raw data) >=20 > 3) Another possibility is to run the algorithms you want to test on a > remote platform without accessing the data and get back the results > (also here there might be some safguards to avoid that program tries to= > extract sensitive data as results !) >=20 > jerome >=20 > Le 31/03/2017 =C3=A0 06:15, Laurent Ciavaglia a =C3=A9crit : >> Also CAIDA has multiple measurement tools to collect data. >> Some IETFers are specialists of these tools and report findings on >> data collected via these tools. >> >> Might be something to look at. >> >> Also, and with this I'm finished for today, documenting the >> characteristics of the requested data set(s) might help. >> >> BR, Laurent. >> >> On 31/03/2017 06:12, Laurent Ciavaglia wrote: >>> Hello, >>> >>> A couple of suggestions: >>> -Why not raise this point / request in various WGs (e.g. in the >>> OPS Area) and RGs (e.g. MAPRG, NMRG). >>> -Why not work with the IETF meeting NOC to collect data sets? >>> >>> BR, Laurent. >>> >>> On 31/03/2017 05:07, Pedro Martinez-Julia wrote: >>>> Hi all, >>>> >>>> That's it and I insist that, if anybody in the mailing list is bound= to >>>> an operator or a similar organization that manages huge amounts of d= ata, >>>> just the data we need, please try to provide it. We will be able to = help >>>> you to build some anonymization solution, if needed. Thank you. >>>> >>>> Regards, >>>> Pedro >>>> >>> >>> --=20 >>> >>> Laurent Ciavaglia >>> >>> Nokia, Bell Labs >>> >>> =20 >>> >>> +33 160 402 636 >>> >>> route de Villejust - Nozay, France >>> >>> linkedin.com/in/laurent.ciavaglia >>> >> >> --=20 >> >> Laurent Ciavaglia >> >> Nokia, Bell Labs >> >> =20 >> >> +33 160 402 636 >> >> route de Villejust - Nozay, France >> >> linkedin.com/in/laurent.ciavaglia >> >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >=20 >=20 >=20 >=20 > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet >=20 From nobody Fri Mar 31 06:48:41 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 9986F1297DE for ; Fri, 31 Mar 2017 06:48:39 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=unicauca.edu.co Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id yqyhGtrU7J5B for ; Fri, 31 Mar 2017 06:48:36 -0700 (PDT) Received: from mail-vk0-x242.google.com (mail-vk0-x242.google.com [IPv6:2607:f8b0:400c:c05::242]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id D412C1297D0 for ; Fri, 31 Mar 2017 06:48:35 -0700 (PDT) Received: by mail-vk0-x242.google.com with SMTP id d188so11404311vka.3 for ; Fri, 31 Mar 2017 06:48:35 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=unicauca.edu.co; s=google; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=a7MgeB/Z7JenUyVYDjPsgHcFpkf79dbN9gDEt1y5OtE=; b=YKaMLGfqmUG+SnG1DYU3QLL83b6+k+tDFGN8Fi3os0wYUjm49oQE4kzie0YjAFlpJ2 7HO23aeag2unjjH/60eOb5B4ZmPhQsmxpkydTVsAm4CGDP27inFYkcySBbUx+R9IhmQ9 YJQi2A298ptDexGCH7XIrOR4lgYxnf8JAimBY= X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=a7MgeB/Z7JenUyVYDjPsgHcFpkf79dbN9gDEt1y5OtE=; b=szojFhEhAF8UHhyNL9w+luxAAWHByqdUuSHCUa6PeNef7Ow2xrTqIXDAZUzmhmRr4s xWnpyMIQ2WUtU1ngwUYzcSxySfXayFyaYWiYhRAgYELSR6hdnMi8sQ0e7jN1OTBxfoG1 8Lz9xN7CM6hwKkLryi08A510xyWatL4lH3N/ACJsm4IlE+uB8l6QHQPt/RA9esREM1gA TULr9Fw/delItTfTxFFQ1pM+fmvW7NyaF1nmv5ScIEU85ZzYE0qV8/85yowhDfSBo0Mj IVlhYZiVu9dkawXkTBt3F5+hRZhrFvNXk3b3hGJe++TJDJy3FAmMIs9mJPCuWZhokhoc w26w== X-Gm-Message-State: AFeK/H184Mhi64pj0zuP7+tmVZbPSThBEIhkssSx354Hou9fLVj3xPKh/ihKUkhjPAX7TrjaJAUub3V9IQ77W3MaqPNggSwETVy5u1u1N04RbRBpr6VvlCf/ex0omauhSwVSUj7s4w== X-Received: by 10.176.64.202 with SMTP id i68mr1545690uad.55.1490968114810; Fri, 31 Mar 2017 06:48:34 -0700 (PDT) MIME-Version: 1.0 Received: by 10.103.44.76 with HTTP; Fri, 31 Mar 2017 06:48:14 -0700 (PDT) In-Reply-To: <52152b22-0178-0531-c26d-bb15a5c53ec8@gmail.com> References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> <0e924c06-b8fb-48fa-5137-f4de120b03db@nokia-bell-labs.com> <52152b22-0178-0531-c26d-bb15a5c53ec8@gmail.com> From: Oscar Mauricio Caicedo Rendon Date: Fri, 31 Mar 2017 08:48:14 -0500 Message-ID: To: Brian E Carpenter Cc: =?UTF-8?B?SsOpcsO0bWUgRnJhbsOnb2lz?= , idnet@ietf.org Content-Type: multipart/alternative; boundary=94eb2c122e6adaebfb054c0710c0 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 13:48:39 -0000 --94eb2c122e6adaebfb054c0710c0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi everyone. I think third option us most viable. On the other hand, in https://www.cl.cam.ac.uk/research/srg/netos/projects/brasil/data/index.html is the BRASIl data set that has been used for IP traffic classification. Furthermore, in next links there are interesting papers using ML for IP traffic classification: - *Lightweight Application Classification for Network Management. * https://www.cl.cam.ac.uk/research/srg/netos/projects/brasil/publications/20= 07-jiang2007lightweight.pdf - *A Machine Learning Approach for Efficient Traffic Classification. * https://www.cl.cam.ac.uk/research/srg/netos/projects/brasil/publications/20= 07-li2007machine.pdf - *Experience with High-Speed Automated Application-Identification for Network-Management. * https://www.cl.cam.ac.uk/research/srg/netos/projects/brasil/publications/at= oz.ancs09.pdf Regards, On Fri, Mar 31, 2017 at 7:47 AM, Brian E Carpenter < brian.e.carpenter@gmail.com> wrote: > > 1) on anonymization: this far from being simple... > > Yes, and another issue is that it may even make the data useless. I hit > that problem a few years ago when evaluating stateless hash algorithms fo= r > use in the IPv6 flow label. The problem was that anonymization would chan= ge > the addresses in a pcap trace in such a way that some regularity in the > original > data would be replaced by pseudo-random bits, which would completely > falsify > the results from a statistical viewpoint. I think that in the same way, M= L > results from anonymized data might be meaningless - and there is no way t= o > know that. > > Regards > Brian > > On 31/03/2017 17:57, J=C3=A9r=C3=B4me Fran=C3=A7ois wrote: > > Hi, > > > > Data is primordial but sharing them is very difficult (I'm not an > > operator) due to privacy but also legislative issues. > > Here are few comments: > > 1) on anonymization: this far from being simple and I do not believe > > that anybody can guarentee that nothing will be leaked out as sensitive > > data (and at the end this will the responsability of the operator who > > have shared the data). > > -> That is why sharing but not in public manner (with a NDA) is still > > the most viable solution (in my opinion) > > > > 2) charaterizing the data we would like to access is clearly a first > > step (even if many of us like to have raw data) > > > > 3) Another possibility is to run the algorithms you want to test on a > > remote platform without accessing the data and get back the results > > (also here there might be some safguards to avoid that program tries to > > extract sensitive data as results !) > > > > jerome > > > > Le 31/03/2017 =C3=A0 06:15, Laurent Ciavaglia a =C3=A9crit : > >> Also CAIDA has multiple measurement tools to collect data. > >> Some IETFers are specialists of these tools and report findings on > >> data collected via these tools. > >> > >> Might be something to look at. > >> > >> Also, and with this I'm finished for today, documenting the > >> characteristics of the requested data set(s) might help. > >> > >> BR, Laurent. > >> > >> On 31/03/2017 06:12, Laurent Ciavaglia wrote: > >>> Hello, > >>> > >>> A couple of suggestions: > >>> -Why not raise this point / request in various WGs (e.g. in the > >>> OPS Area) and RGs (e.g. MAPRG, NMRG). > >>> -Why not work with the IETF meeting NOC to collect data sets? > >>> > >>> BR, Laurent. > >>> > >>> On 31/03/2017 05:07, Pedro Martinez-Julia wrote: > >>>> Hi all, > >>>> > >>>> That's it and I insist that, if anybody in the mailing list is bound > to > >>>> an operator or a similar organization that manages huge amounts of > data, > >>>> just the data we need, please try to provide it. We will be able to > help > >>>> you to build some anonymization solution, if needed. Thank you. > >>>> > >>>> Regards, > >>>> Pedro > >>>> > >>> > >>> -- > >>> > >>> Laurent Ciavaglia > >>> > >>> Nokia, Bell Labs > >>> > >>> > >>> > >>> +33 160 402 636 > >>> > >>> route de Villejust - Nozay, France > >>> > >>> linkedin.com/in/laurent.ciavaglia > >>> > >> > >> -- > >> > >> Laurent Ciavaglia > >> > >> Nokia, Bell Labs > >> > >> > >> > >> +33 160 402 636 > >> > >> route de Villejust - Nozay, France > >> > >> linkedin.com/in/laurent.ciavaglia > >> > >> > >> > >> _______________________________________________ > >> IDNET mailing list > >> IDNET@ietf.org > >> https://www.ietf.org/mailman/listinfo/idnet > > > > > > > > > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --=20 *Oscar Mauricio Caicedo Rend=C3=B3n* *PhD Computer Science - Federal University of Rio Grande do Sul* *Full Profesor - University of Cauca* --=20 ------------------------------ *Universidad del Cauca: Comprometidos con la calidad.* --94eb2c122e6adaebfb054c0710c0 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hi everyone.

I think third opt= ion us most viable.

On the other hand, in https:/= /www.cl.cam.ac.uk/research/srg/netos/projects/brasil/data/index.html is= the BRASIl data set that has been used for IP traffic classification. Furt= hermore, in next links there are interesting papers using ML for IP traffic= classification:

- Lightweight Application Classification for Net= work Management. https://www.cl= .cam.ac.uk/research/srg/netos/projects/brasil/publications/2007-jiang2007li= ghtweight.pdf

- A Machine Learning Approach for Efficient Tra= ffic Classification. https://www.cl.ca= m.ac.uk/research/srg/netos/projects/brasil/publications/2007-li2007machine.= pdf


- Experience with High-Speed Automated Application-Id= entification for Network-Management. https://= www.cl.cam.ac.uk/research/srg/netos/projects/brasil/publications/atoz.ancs0= 9.pdf

Regards,





On Fri, Mar 31, 2017 at = 7:47 AM, Brian E Carpenter <brian.e.carpenter@gmail.com><= /span> wrote:
> 1) on anonymization: t= his far from being simple...

Yes, and another issue is that it may even make the data useless. I hit
that problem a few years ago when evaluating stateless hash algorithms for<= br> use in the IPv6 flow label. The problem was that anonymization would change=
the addresses in a pcap trace in such a way that some regularity in the ori= ginal
data would be replaced by pseudo-random bits, which would completely falsif= y
the results from a statistical viewpoint. I think that in the same way, ML<= br> results from anonymized data might be meaningless - and there is no way to<= br> know that.

Regards
=C2=A0 =C2=A0Brian

On 31/03/2017 17:57, J=C3=A9r=C3=B4me Fran=C3=A7ois wrote:
> Hi,
>
> Data is primordial but sharing them is very difficult (I'm not an<= br> > operator) due to privacy but also legislative issues.
> Here are few comments:
> 1) on anonymization: this far from being simple and I do not believe > that anybody can guarentee that nothing will be leaked out as sensitiv= e
> data (and at the end this will the responsability of the operator who<= br> > have shared the data).
> -> That is why sharing but not in public manner (with a NDA) is sti= ll
> the most viable solution (in my opinion)
>
> 2) charaterizing the data we would like to access is clearly a first > step (even if many of us like to have raw data)
>
> 3) Another possibility is to run the algorithms you want to test on a<= br> > remote platform without accessing the data and get back the results > (also here there might be some safguards to avoid that program tries t= o
> extract sensitive data as results !)
>
> jerome
>
> Le 31/03/2017 =C3=A0 06:15, Laurent Ciavaglia a =C3=A9crit :
>> Also CAIDA has multiple measurement tools to collect data.
>> Some IETFers are specialists of these tools and report findings on=
>> data collected via these tools.
>>
>> Might be something to look at.
>>
>> Also, and with this I'm finished for today, documenting the >> characteristics of the requested data set(s) might help.
>>
>> BR, Laurent.
>>
>> On 31/03/2017 06:12, Laurent Ciavaglia wrote:
>>> Hello,
>>>
>>> A couple of suggestions:
>>>=C2=A0 =C2=A0 =C2=A0-Why not raise this point / request in vari= ous WGs (e.g. in the
>>> OPS Area) and RGs (e.g. MAPRG, NMRG).
>>>=C2=A0 =C2=A0 =C2=A0-Why not work with the IETF meeting NOC to = collect data sets?
>>>
>>> BR, Laurent.
>>>
>>> On 31/03/2017 05:07, Pedro Martinez-Julia wrote:
>>>> Hi all,
>>>>
>>>> That's it and I insist that, if anybody in the mailing= list is bound to
>>>> an operator or a similar organization that manages huge am= ounts of data,
>>>> just the data we need, please try to provide it. We will b= e able to help
>>>> you to build some anonymization solution, if needed. Thank= you.
>>>>
>>>> Regards,
>>>> Pedro
>>>>
>>>
>>> --
>>>
>>> Laurent Ciavaglia
>>>
>>> Nokia, Bell Labs
>>>
>>>
>>>
>>> +33 160 402 636
>>>
>>> route de Villejust - Nozay, France
>>>
>>> linkedin.com/in/laurent.ciavaglia
>>>
>>
>> --
>>
>> Laurent Ciavaglia
>>
>> Nokia, Bell Labs
>>
>>
>>
>> +33= 160 402 636
>>
>> route de Villejust - Nozay, France
>>
>> linkedin.com/in/laurent.ciavaglia
>>
>>
>>
>> _______________________________________________
>> IDNET mailing list
>> IDNET@ietf.org
>> https://www.ietf.org/mailman/listinfo/idnet=
>
>
>
>
> _______________________________________________
> IDNET mailing list
> IDNET@ietf.org
> https://www.ietf.org/mailman/listinfo/idnet=
>

_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet



--
Oscar Mauricio Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0= Federal University of Rio Gran= de do Sul
Full Profesor - University of Cauca

= Universidad del Cauca: Comprometidos = con la calidad.
 --94eb2c122e6adaebfb054c0710c0-- From nobody Fri Mar 31 08:06:57 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 73C62129511 for ; Fri, 31 Mar 2017 08:06:56 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.201 X-Spam-Level: X-Spam-Status: No, score=-4.201 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id V51iYimuikKs for ; Fri, 31 Mar 2017 08:06:54 -0700 (PDT) Received: from ns1.nict.go.jp (ns1.nict.go.jp [IPv6:2001:df0:232:300::1]) by ietfa.amsl.com (Postfix) with ESMTP id C43E51294EF for ; Fri, 31 Mar 2017 08:06:53 -0700 (PDT) Received: from gw1.nict.go.jp (gw1.nict.go.jp [133.243.18.250]) by ns1.nict.go.jp with ESMTP id v2VF6qDL071092 for ; Sat, 1 Apr 2017 00:06:52 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw1.nict.go.jp with ESMTP id v2VF6q09071087 for ; Sat, 1 Apr 2017 00:06:52 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id 6D1DDA776 for ; Sat, 1 Apr 2017 00:06:51 +0900 (JST) Date: Sat, 1 Apr 2017 00:06:46 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170331150646.GI4808@spectre> References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> <20170331030720.GF4808@spectre> <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: <9a761473-d188-5a1a-d31e-6ad991dfd1e5@nokia-bell-labs.com> User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith1 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 15:06:56 -0000 On Fri, Mar 31, 2017 at 06:12:19AM +0200, Laurent Ciavaglia wrote: > Hello, > > A couple of suggestions: > -Why not raise this point / request in various WGs (e.g. in the OPS > Area) and RGs (e.g. MAPRG, NMRG). > -Why not work with the IETF meeting NOC to collect data sets? Dear Laurent and others, The main reason I did not consider to request it within/through the IETF is that I do not know how it would be addressed, so I cannot help in any aspect. Since I somehow know (have experience with) the process required to get access to information from operators, I encouraged it. That said, and considering that operators did not demonstrate interest on this topic, I request the list that if somebody knows how to address this request within/through the IETF, please let us know so we can work together to get it done as best as possible. It would mean a lot for the network research and engineering community, inside and outside IETF, so it is worth to try. Also, please, consider that any kind of data would be worth having. Even if anonymization breaks something or most of it, since it would be much more useful than synthetic data. Remember that this is about AI, so the proposed models and algorithms should also work with adulterated data to some extent. They would be later validated and tuned using better data will less (or none) adulteration. To sum up, any real information is worth to have, regardless of it being adulterated, filtered, or anything. If anybody knows some way of getting it or has some examples, please point us to them, as others have already done, or give us some "easy" guide of how to get the access to it. Thank you very much. > BR, Laurent. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Fri Mar 31 08:08:26 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 022A41294ED for ; Fri, 31 Mar 2017 08:08:24 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.598 X-Spam-Level: X-Spam-Status: No, score=-2.598 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, URIBL_BLOCKED=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id L3aAXNu4I3zi for ; Fri, 31 Mar 2017 08:08:21 -0700 (PDT) Received: from mail-qk0-x236.google.com (mail-qk0-x236.google.com [IPv6:2607:f8b0:400d:c09::236]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id C32541294EF for ; Fri, 31 Mar 2017 08:08:20 -0700 (PDT) Received: by mail-qk0-x236.google.com with SMTP id d201so39425721qkc.0 for ; Fri, 31 Mar 2017 08:08:20 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=cPb6Nd8GnkXQVqbnD1Uft790bVbAIg5cQNRB+oc183o=; b=queLnwYicURpmpvtO5m3SHwwBdXOfKvSpp8+uSWCpqJ3HmUBOkrudY2BgJ8wwmnUet 5wiBFrugetSx9d7zoKO0RXKU/acsoTciAaunwT3UvekfupIaiUfYtDnUADkHJUuNXi16 k+xGKWxnMzMb/5b/rJtYqJ6o+01m0SydcIbpprxaRI1CGXz3LBqZT30JDiRg1OO+Q7fJ e97ou+AQjZ0hNh6WuAF3dpcnwyEYeJahcbkNBXKrN1I3zDeqtW+TJEKb7EWf6Az1kphx II1qHfX0D3DGWzewsQiyk02OJTRcsqqRZxOwxXQ6GnT8w+Itg02Mvv4H9gsYDhpLZ5BL g77Q== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=cPb6Nd8GnkXQVqbnD1Uft790bVbAIg5cQNRB+oc183o=; b=LXkTXHk7tKl5hiYrJF71CMF4Bg8lWf8ZHPqgUQMMZOrAouy0N9B2YIdWb0WauJEzJc IAVq4lg8hglFYEMvIhhRi9SP43ShCxary0kqbJ1jNxgVA8qB+Bfhc3+MrIUXrEJwH5Qr ax1HcKhuUQqoVM9NxLEZpaTfAxQUDW3W1UHJ38KZZT/1l0v+jqEW9JvRpjwMN4GJ3I9r WIO46I+og04rHTX6LwwCgPGnAkCSZSZlRyQtr9sgY3uXFtFY0KGWe6Wbs1HwIZRGAg6q lyy/z/VCIsxIfU2H3ozxsNb8vhKUzCuGJhFAdk6o6M85+8MmVVyFLzSwV6vJgwixoNiz 4j3w== X-Gm-Message-State: AFeK/H0qZh1VZhYClIYK841jQpbcqLOk6vqDBuMcGW6P/3me0QWxiqA84jZSzrvTDjv38HgB0nCai8i7o7JqPQ== X-Received: by 10.55.65.81 with SMTP id o78mr2976710qka.82.1490972899749; Fri, 31 Mar 2017 08:08:19 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Fri, 31 Mar 2017 08:08:18 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> References: <3B110B81B721B940871EC78F107D848CF33029@DGGEMM506-MBS.china.huawei.com> From: David Meyer Date: Fri, 31 Mar 2017 08:08:18 -0700 Message-ID: To: "dingxiaojian (A)" Cc: Brian E Carpenter , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a114ac54e0f770b054c082e58 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 31 Mar 2017 15:08:24 -0000 --001a114ac54e0f770b054c082e58 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I don't think there is any question that there is a problem with data sets; as many of you know this has been one of my main points regarding what is holding back ML for networking over the last 4 or so years. Of course, how to solve this problem is a different question. However, as least I don't necessarily agree with this statement: " So my point is data set, not ML model. We can select some existing models (SVM, ELM, bayes, etc) to learn different tasks. We do not research the principle of ML algorithm, but use them to slove [sic] network problems", we might check out what Andrew says while discussing what the scare resources required to make progress in ML for any domain ([0], near the bottom): o Data. Among leading AI teams, many can likely replicate others=E2=80=99 s= oftware in, at most, 1=E2=80=932 years. But it is exceedingly difficult to get acce= ss to someone else=E2=80=99s data. Thus data, rather than software, is the defens= ible barrier for many businesses. o Talent. Simply downloading and =E2=80=9Capplying=E2=80=9D open-source sof= tware to your data won=E2=80=99t work. AI needs to be customized to your business context= and data. This is why there is currently a war for the scarce AI talent that can do this work. There is a ton of experience (and literature) reinforcing these points, but suffice it to say that Andrew knows what he's talking about. In any event, it seems clear that we are all on the same page with respect to the problems with data sets (again, exactly how to solve this problem is open). However, we seem to have a difference in our understanding of both the field is today and how we make progress. In particular, Andrew seems to be saying the opposite of what you say above (quoted text). My experience tracks more with what Andrew is saying. Summary: We need to attack both problems. Dave [0] https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-= now On Thu, Mar 30, 2017 at 6:04 PM, dingxiaojian (A) wrote: > Hi Brian, > You are right. > I have worked in an institute more than five years. The main work is > use ML to solve the domain problem. However, for privacy reason, it's ve= ry > hard to get some real domain data sets. So the results learned by any ML > models is not reliable. > So I think the important/first thing we do is to construct real and > reliable data sets of network domain. Just like UCI repository ( > https://archive.ics.uci.edu/ml/datasets.html) or images datasets ( > https://www.cs.utah.edu/~lifeifei/datasets.html) . Only the common and > real data sets are agreed with all we, different ML models can be applied > to validate and predict. > So my point is data set, not ML model. We can select some existing > models (SVM, ELM, bayes, etc) to learn different tasks. We do not researc= h > the principle of ML algorithm, but use them to slove network problems. > > Best regards, > > Xiaojian > > > -----=E9=82=AE=E4=BB=B6=E5=8E=9F=E4=BB=B6----- > =E5=8F=91=E4=BB=B6=E4=BA=BA: IDNET [mailto:idnet-bounces@ietf.org] =E4=BB= =A3=E8=A1=A8 Brian E Carpenter > =E5=8F=91=E9=80=81=E6=97=B6=E9=97=B4: 2017=E5=B9=B43=E6=9C=8830=E6=97=A5 = 23:37 > =E6=94=B6=E4=BB=B6=E4=BA=BA: Sheng Jiang ; David M= eyer > =E6=8A=84=E9=80=81: idnet@ietf.org > =E4=B8=BB=E9=A2=98: Re: [Idnet] Intelligence-Defined Network Architecture= and Call for > Interests > > Agreed, and there are (still) two key points: > > 1. What is our underlying model (what Dave called a "theory of > networking")? With no such model, it's very hard to tell the ML system wh= at > to do. > > 2. And as others have said: get hold of large datasets that can processed > by ML according to that model. For developing open solutions, a corpus of > open data sets seems essential. As anybody from the network measurement > community will tell you, getting hold of large data sets from operators i= s > extremely difficult for both privacy and commercial reasons. > > Brian > > > On 31/03/2017 03:41, Sheng Jiang wrote: > > Hi, David, > > > > I think I agree with you, but in slight different expression. Yes, the > hard parts of getting ML into Network lies on machine learning. But, it i= s > not that we need to develop any new ML technical/algorithms for networkin= g > in particular. It is that we MUST re-set up our network domain knowledge > from the perspective of applying ML. My slides [0] does not suggest that > *someone else* will handle the ML part. Actually, oppositely, it suggests > some experts who have knowledge of both ML and network (probably we) woul= d > develop tools/algorithms/systems to handle the ML part for other network > experts (more than 98 percent of current network administrators). So that= , > these network experts would be allowed to manage their network easily wit= h > intelligence association, but no need to become ML experts themselves. > Here, we would like to treat the network administrators like the users in > other successful ML application. We are the domian experts to do the dirt= y > AI work for them. > > > > I believe we have common understanding in the above description. But > certainly my slides needs further refine to clarify my viewpoint. > > > > Best regards, > > > > Sheng > > ________________________________ > > From: IDNET [idnet-bounces@ietf.org] on behalf of David Meyer > > [dmm@1-4-5.net] > > Sent: 29 March 2017 2:01 > > To: Sheng Jiang > > Cc: idnet@ietf.org > > Subject: Re: [Idnet] Intelligence-Defined Network Architecture and > > Call for Interests > > > > s/NMRL/NMLRG/ (sorry about that). Dave > > > > On Tue, Mar 28, 2017 at 10:59 AM, David Meyer dmm@1-4-5.net>> wrote: > > Hey Sheng, > > > > I just wanted to revive my key concern on [0] (same one I made at the > NMRL): The hard parts of getting Machine Learning intelligence into > Networking is the Machine Learning part. In addition, successful deployme= nt > of ML requires knowledge of ML combined with domain knowledge. We > definitely have the domain knowledge; the problem is that we don't have t= he > ML knowledge, and this is one of the big factors holding us back; see e.g= . > Andrew's discussion of talent in [1]. Slides such as [0] seem to imply > that *someone else* (in particular, not us) will handle the ML part of a= ll > of this. I'll just note that in general successful deployments of ML don'= t > work this way; the domain experts will have to learn ML (and vice versa) > for us to be successful (again, see [1] and many others). > > > > Perhaps a useful exercise would be to write an ID that makes your > assumptions explicit? > > > > Thanks, > > > > Dave > > > > > > [0] > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc > > e-defined-network-01.pdf [1] > > https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-r > > ight-now > > > > > > On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang > wrote: > > Hi, all, > > > > Although there are many understanding for Intelligence-Defined Network, > we are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc > > e-defined-network-01.pdf > > > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com > . Then we may have an informal meeting to discuss some common interests a= nd > potential future activities (not any activities in IETF, but also other S= TO > or experimental trails, etc.) on Thursday morning. > > > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID= =3D > > 51011 > > > > Meanwhile, please do use this mail list as a forum to discuss any topic= s > that may applying AI/ML into network area. > > > > Best regards, > > > > Sheng > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > > > > > > > > > _______________________________________________ > > IDNET mailing list > > IDNET@ietf.org > > https://www.ietf.org/mailman/listinfo/idnet > > > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --001a114ac54e0f770b054c082e58 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
I don't think there is any question that there is a pr= oblem with data sets; as many of you know this has been one of my main poin= ts regarding what is holding back ML for networking over the last 4 or so y= ears. Of course, how to solve this problem is a different question.=C2=A0
However, as least I don't necessarily agree with this= statement: "=C2=A0So my point is data set, not ML model.=C2=A0 We can= select some existing models (SVM, ELM, bayes, etc) to learn different task= s. We do not research the principle of ML algorithm, but use them to slove = [sic] network problems", =C2=A0we might check out what Andrew says whi= le discussing what the scare resources required to make progress in ML for = any domain ([0], near the bottom):

o Data. Among le= ading AI teams, many can likely replicate others=E2=80=99 software in, at m= ost, 1=E2=80=932 years. But it is exceedingly difficult to get access to so= meone else=E2=80=99s data. Thus data, rather than software, is the defensib= le barrier for many businesses.

o Talent. Simply d= ownloading and =E2=80=9Capplying=E2=80=9D open-source software to your data= won=E2=80=99t work. AI needs to be customized to your business context and= data. This is why there is currently a war for the scarce AI talent that c= an do this work.

There is a ton of experience= (and literature) reinforcing these points, but suffice it to say that Andr= ew knows what he's talking about.

In any event= , it seems clear that we are all on the same page with respect to the probl= ems with data sets (again, exactly how to solve this problem is open). Howe= ver, =C2=A0we seem to have a difference in our understanding of both the fi= eld is today and how we make progress. In particular, Andrew seems to be sa= ying the opposite of what you say above (quoted text). My experience tracks= more with what Andrew is saying.

Summary: =C2=A0W= e need to attack both problems.=C2=A0


On Thu, Mar 30, 2017 at 6:04 PM, dingxiaojian (A) <= dingxiaojian1= @huawei.com> wrote:
Hi Bria= n,
=C2=A0 =C2=A0 You are right.
=C2=A0 =C2=A0 =C2=A0I have worked in an institute more than five years. The= main work is=C2=A0 use ML to solve the domain problem.=C2=A0 However, for = privacy reason, it's very hard to get some real domain data sets. So th= e results learned by any ML models is not reliable.
=C2=A0 =C2=A0 So I think the important/first thing we do is to construct re= al and reliable data sets of network domain. Just like UCI repository (https://archive.ics.uci.edu/ml/datasets.html) or im= ages datasets (https://www.cs.utah.edu/~lifeifei= /datasets.html) . Only the common and real data sets are agreed with al= l we, different ML models can be applied to validate and predict.
=C2=A0 =C2=A0So my point is data set, not ML model.=C2=A0 We can select som= e existing models (SVM, ELM, bayes, etc) to learn different tasks. We do no= t research the principle of ML algorithm, but use them to slove network pro= blems.

=C2=A0Best regards,

Xiaojian


-----=E9=82=AE=E4=BB=B6=E5=8E=9F=E4=BB=B6-----
=E5=8F=91=E4=BB=B6=E4=BA=BA: IDNET [mailto:idnet-bounces@ietf.org] =E4=BB=A3=E8=A1=A8 Brian E Carpe= nter
=E5=8F=91=E9=80=81=E6=97=B6=E9=97=B4: 2017=E5=B9=B43=E6=9C=8830=E6=97=A5 23= :37
=E6=94=B6=E4=BB=B6=E4=BA=BA: Sheng Jiang <jiangsheng@huawei.com>; David Meyer <dmm@1-4-5.net>
=E6=8A=84=E9=80=81: idnet@ietf.org =E4=B8=BB=E9=A2=98: Re: [Idnet] Intelligence-Defined Network Architecture a= nd Call for Interests

Agreed, and there are (still) two key points:

1. What is our underlying model (what Dave called a "theory of network= ing")? With no such model, it's very hard to tell the ML system wh= at to do.

2. And as others have said: get hold of large datasets that can processed b= y ML according to that model. For developing open solutions, a corpus of op= en data sets seems essential. As anybody from the network measurement commu= nity will tell you, getting hold of large data sets from operators is extre= mely difficult for both privacy and commercial reasons.

=C2=A0 =C2=A0Brian


On 31/03/2017 03:41, Sheng Jiang wrote:
> Hi, David,
>
> I think I agree with you, but in slight different=C2=A0 expression. Ye= s, the hard parts of getting ML into Network lies on machine learning. But,= it is not that we need to develop any new ML technical/algorithms for netw= orking in particular. It is that we MUST re-set up our network domain knowl= edge from the perspective of applying ML. My slides [0] does not suggest th= at *someone else* will handle the ML part. Actually, oppositely, it suggest= s some experts who have knowledge of both ML and network (probably we) woul= d develop tools/algorithms/systems to handle the ML part for other network = experts (more than 98 percent of current network administrators). So that, = these network experts would be allowed to manage their network easily with = intelligence association, but no need to become ML experts themselves. Here= , we would like to treat the network administrators like the users in other= successful ML application. We are the domian experts to do the dirty AI wo= rk for them.
>
> I believe we have common understanding in the above description. But c= ertainly my slides needs further refine to clarify my viewpoint.
>
> Best regards,
>
> Sheng
> ________________________________
> From: IDNET [idnet-bounces@i= etf.org] on behalf of David Meyer
> [dmm@1-4-5.net]
> Sent: 29 March 2017 2:01
> To: Sheng Jiang
> Cc: idnet@ietf.org
> Subject: Re: [Idnet] Intelligence-Defined Network Architecture and
> Call for Interests
>
> s/NMRL/NMLRG/=C2=A0 =C2=A0(sorry about that). Dave
>
> On Tue, Mar 28, 2017 at 10:59 AM, David Meyer <dmm@1-4-5.net<mailto:d= mm@1-4-5.net>> wrote:
> Hey Sheng,
>
> I just wanted to revive my key concern on [0] (same one I made at the = NMRL): The hard parts of getting Machine Learning intelligence into Network= ing is the Machine Learning part. In addition, successful deployment of ML = requires knowledge of ML combined with domain knowledge. We definitely have= the domain knowledge; the problem is that we don't have the ML knowled= ge, and this is one of the big factors holding us back; see e.g. Andrew'= ;s discussion of talent in [1].=C2=A0 Slides such as [0] seem to imply that= *someone else* (in particular, not us)=C2=A0 will handle the ML part of al= l of this. I'll just note that in general successful deployments of ML = don't work this way; the domain experts will have to learn ML (and vice= versa) for us to be successful (again, see [1] and many others).
>
> Perhaps a useful exercise would be to write an ID that makes your assu= mptions explicit?
>
> Thanks,
>
> Dave
>
>
> [0]
> https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc
> e-defined-network-01.pdf [1]
> https://hbr.org/2016/11/w= hat-artificial-intelligence-can-and-cant-do-r
> ight-now
>
>
> On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang <jiangsheng@huawei.com<mailto:jiangsheng@huawei.com>> wrote:
> Hi, all,
>
> Although there are many understanding for Intelligence-Defined Network= , we are actually using this IDN as a term reference to the SDN-beyond arch= itecture that we presented in IETF97, see the below link. A reference model= is presented in page 3, while potential standardization works is presented= in page 9.
>
> https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligenc
> e-defined-network-01.pdf
>
> Although it might be a little bit too early for AI/ML in network givin= g the recent story of the concluded proposed NMLRG, we still would like to = call for interests in IDN. Anybody (on site in Chicago this week) are inter= ested in this or even wider topics regarding to AI/ML in network, please co= ntact me on jiangsheng@huawei.com<= /a><mailto:jiangsheng@huaw= ei.com> . Then we may have an informal meeting to discuss some commo= n interests and potential future activities (not any activities in IETF, bu= t also other STO or experimental trails, etc.)=C2=A0 on Thursday morning. >
> FYI, we have already working on a Work Item, called IDN in the ETSI NG= P (Next Generation Protocol) ISG, links below.
>
> https://portal.etsi.org/tb.aspx?= tbid=3D844&SubTB=3D844
> https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D
> 51011
>
> Meanwhile, please do use this mail list as a forum to discuss any topi= cs that may applying AI/ML into network area.
>
> Best regards,
>
> Sheng
> _______________________________________________
> IDNET mailing list
> IDNET@ietf.org<mailto:IDNET@ietf.org>
> https://www.ietf.org/mailman/listinfo/idnet=
>
>
>
>
>
> _______________________________________________
> IDNET mailing list
> IDNET@ietf.org
> https://www.ietf.org/mailman/listinfo/idnet=
>

_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--001a114ac54e0f770b054c082e58--

Hey Henk,


On Thu, Mar 23, 2017 at 2:55 AM, Henk Birk= holz <henk.birkholz@sit.fraunhofer.de> wrote:<= br>
Hello,

maybe an excerpt from my personal point of view can be a contribution to th= e discussion starting on this list.


In my experience, the gap between... work-flows (in lack of a better term) = how problem statements are created in the domain of network/management/secu= rity and - in contrast - the domain of machine learning is (aka "appea= rs to me, subjectively") astonishingly vast.

=
Definitely. See the few attached slides for a some ideas I have = on the topic from upcoming talks.
=C2=A0

One way to illustrate that gap (and there are multiple ways, I think), usin= g a bit of hyperbole:

"network: least viable solution" -> "I only produce the i= nformation I
require"

meets

"machine learning: combination of heterogeneous most viable solutions&= quot; -> "Please provide me with everything you got, including the = best qualified semantic annotation of characteristics and context so I can = identify and select the features that are relevant to provide a contributio= n".


Also - as trivial and repetitive as that might sound - terminology again is= key. Please note the following quote I actually encountered in my very ear= ly days collaborating with machine learning architects: "The maximum n= umber of ports really is 2^16? Wow, how big can these routers be?". While this is of course "one of these entertaining anecdotes" eve= rybody already heard at least once already, it also highlights quite promin= ently the existing gap form a different angle.


In consequence, guidance that enables an individual with a specialization i= n machine learning skills to just better understand the network domain itse= lf - maybe by illustrating very simple, well-known and already solved probl= em statements - might already be a contribution of high value, just because= it is specifically provided for that group of individuals and using termin= ology that is common and well-understood in that domain.

I think what you are saying is that domain knowledge is v= ery important (key) when building ML solutions. That is for sure true.=C2= =A0

Regarding terminology: it is a mess in ML: eve= ryone uses their own notation. Consider the notation used in [0] vs. for ex= ample, the Deep Learning Book [1]. Compare the notation in [0] to say, Chap= ter 6 of [1]. So we don't yet agree even on mathematical notation (thou= gh the notation of [0] isn't widely used) much less the description of = networks. It goes on. I'll just point out here that this again goes to = not having a "usable" theory of network (UTON, I guess :-)).

Thanks,

Dave




Viele Gr=C3=BC0e,

Henk


On 03/22/2017 06:29 PM, David Meyer wrote:
Folks,

I thought I'd try to get some discussion going by outlining some of my<= br> views as to why networking is lagging other areas in the development and application of Machine Learning (ML). In particular, networking is way
behind what we might call the "perceptual tasks" (vision, NLP, ro= botics,
etc) as well as other areas (medicine, finance, ...). The attached slide from one of my decks tries to summarize the situation, but I'll give a<= br> bit of an outline below.

So why is networking lagging many other fields when it comes to the
application of machine learning? There are several reasons which I'll try to outline here (I was fortunate enough to discuss this with the
packetpushers crew a few weeks ago, see [0]). These are in no particular order.

First, we don't have a "useful" theory of networking (UTON). = One way to
think about what such a theory would look like is by analogy to what we
see with the success of convolutional neural networks (CNNs) not only
for vision but now for many other tasks. In that case there is a theory
of how vision works, built up from concepts like receptive fields,
shared weights, simple and complex cells, etc. For example, the input
layer of a CNN isn't fully connected; rather connections reflect the receptive field of the input layer, which is in a way that is "inspire= d"
by biological vision (being very careful with "biological inspiration&= quot;).
Same with the alternation of convolutional and pooling layers; these
loosely model the alternation of simple and complex cells in the primary visual cortex (V1), the secondary visual cortex(V2) and the Brodmann
area (V3). BTW, such a theory seems to be required for transfer learning [1], which we'll need if we don't want every network to be analyzed= in
an ad-hoc, one-off style (like we see today).

The second thing that we need to think about is publicly available
standardized data sets. Examples here include MNIST, ImageNet, and many
others. The result of having these data sets has been the
steady ratcheting down of error rates on tasks such as object and scene
recognition, NLP, and others to super-human levels. Suffice it to say we have nothing like these data sets for networking. Networking data sets
today are largely proprietary, and because there is no UTON, there is no real way to compare results between them.

Third, there is a large skill set gap. Network engineers (us!) typically don't have the mathematical background required to build effective
machine learning at scale. See [2] for an outline of some of the
mathematical skills that are essential for effective ML. There is a lot
more to this, involving how progress is made in ML (open data, open
source, open models, in general open science and associated communities, see e.g., OpenAi [3], Distill [4], and many others). In any event we
need build community and gain new skills if we want to be able to
develop and apply state of the art machine learning algorithms to
network data, at scale. The bottom line is that it will be difficult if
not impossible to be effective in the ML space if we ourselves don't understand how it works and further, if we can build explainable systems (noting that explaining what the individual neurons in a deep neural
network are doing is notoriously difficult; that said much progress is
being made). So we want to build explainable, end-to-end trained
systems, and to accomplish this we ourselves need to understand how
these algorithms work, but in training and in inference.

This email is already TL;DR but I'll add one more here: We need to lear= n
control, not just prediction. Since we live in an inherently adversarial environment we need to take advantage of Reinforcement Learning as well
as the various attacks being formulated against ML; [5] gives one
interesting example of attacks against policy networks using adversarial examples. See also slides 31 and 32 of [6] for some more on this topic.

I hope some of this gets us thinking about the problems we need to solve in order to be successful in the ML space. There's plenty more of this<= br> on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~= dmm/vita.html.
I'm looking forward to the discussion.

Thanks,

--dmm




[0]=C2=A0 http://packetpushers.net/podcast/podcasts/pq-show-107-applicability-machine-learning-networking/

[1]=C2=A0 http://sebastianruder.com/trans= fer-learning/index.html
[2]=C2=A0 http://datascience.ibm= .com/blog/the-mathematics-of-machine-learning/
[3] https://openai.com/blog/
[4] ht= tp://distill.pub/
[5] http://rll.berkeley.edu/adve= rsarial/arXiv2017_AdversarialAttacks.pdf
[6]=C2=A0 http://www.1-4-5.net/~dmm/ml= /talks/2016/cor_ml4networking.pptx



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header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id VjqUMWZCjP-S for ; Thu, 23 Mar 2017 07:16:42 -0700 (PDT) Received: from mail-qt0-x22d.google.com (mail-qt0-x22d.google.com [IPv6:2607:f8b0:400d:c0d::22d]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id ED1C81267BB for ; Thu, 23 Mar 2017 07:16:41 -0700 (PDT) Received: by mail-qt0-x22d.google.com with SMTP id r45so176198441qte.3 for ; Thu, 23 Mar 2017 07:16:41 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to; bh=aguJj/rqcb3dgZC4NGSaNhTMhIlsdalxaDtWrnqI2oc=; b=W1MdMHdP/uUIFSvUvILzSLssdfpYTP2JjwAeHWZLxlETMnOuUg4Bc6i8eoma5P7/y4 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with SMTP id z5mr2552613qtd.203.1490278600649; Thu, 23 Mar 2017 07:16:40 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Thu, 23 Mar 2017 07:16:39 -0700 (PDT) X-Originating-IP: [98.207.11.149] In-Reply-To: References: From: David Meyer Date: Thu, 23 Mar 2017 07:16:39 -0700 Message-ID: To: idnet@ietf.org Content-Type: multipart/alternative; boundary=94eb2c1252bc9bb956054b6686d2 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 23 Mar 2017 14:16:45 -0000 --94eb2c1252bc9bb956054b6686d2 Content-Type: text/plain; charset=UTF-8 Interestingly, Andrew also points out the need for data sets and the problem with talent pools (among many other things): https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now --dmm On Wed, Mar 22, 2017 at 10:29 AM, David Meyer wrote: > Folks, > > I thought I'd try to get some discussion going by outlining some of my > views as to why networking is lagging other areas in the development and > application of Machine Learning (ML). In particular, networking is way > behind what we might call the "perceptual tasks" (vision, NLP, robotics, > etc) as well as other areas (medicine, finance, ...). The attached slide > from one of my decks tries to summarize the situation, but I'll give a bit > of an outline below. > > So why is networking lagging many other fields when it comes to the > application of machine learning? There are several reasons which I'll try > to outline here (I was fortunate enough to discuss this with the > packetpushers crew a few weeks ago, see [0]). These are in no particular > order. > > First, we don't have a "useful" theory of networking (UTON). One way to > think about what such a theory would look like is by analogy to what we see > with the success of convolutional neural networks (CNNs) not only for > vision but now for many other tasks. In that case there is a theory of how > vision works, built up from concepts like receptive fields, shared weights, > simple and complex cells, etc. For example, the input layer of a CNN isn't > fully connected; rather connections reflect the receptive field of the > input layer, which is in a way that is "inspired" by biological vision > (being very careful with "biological inspiration"). Same with the > alternation of convolutional and pooling layers; these loosely model the > alternation of simple and complex cells in the primary visual cortex (V1), > the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a > theory seems to be required for transfer learning [1], which we'll need if > we don't want every network to be analyzed in an ad-hoc, one-off style > (like we see today). > > The second thing that we need to think about is publicly available > standardized data sets. Examples here include MNIST, ImageNet, and many > others. The result of having these data sets has been the steady ratcheting > down of error rates on tasks such as object and scene recognition, NLP, and > others to super-human levels. Suffice it to say we have nothing like these > data sets for networking. Networking data sets today are largely > proprietary, and because there is no UTON, there is no real way to compare > results between them. > > Third, there is a large skill set gap. Network engineers (us!) typically > don't have the mathematical background required to build effective machine > learning at scale. See [2] for an outline of some of the mathematical > skills that are essential for effective ML. There is a lot more to this, > involving how progress is made in ML (open data, open source, open models, > in general open science and associated communities, see e.g., OpenAi [3], > Distill [4], and many others). In any event we need build community and > gain new skills if we want to be able to develop and apply state of the art > machine learning algorithms to network data, at scale. The bottom line is > that it will be difficult if not impossible to be effective in the ML space > if we ourselves don't understand how it works and further, if we can build > explainable systems (noting that explaining what the individual neurons in > a deep neural network are doing is notoriously difficult; that said much > progress is being made). So we want to build explainable, end-to-end > trained systems, and to accomplish this we ourselves need to understand how > these algorithms work, but in training and in inference. > > This email is already TL;DR but I'll add one more here: We need to learn > control, not just prediction. Since we live in an inherently adversarial > environment we need to take advantage of Reinforcement Learning as well as > the various attacks being formulated against ML; [5] gives one interesting > example of attacks against policy networks using adversarial examples. See > also slides 31 and 32 of [6] for some more on this topic. > > I hope some of this gets us thinking about the problems we need to solve > in order to be successful in the ML space. There's plenty more of this on > http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. > I'm looking forward to the discussion. > > Thanks, > > --dmm > > > > > [0] http://packetpushers.net/podcast/podcasts/pq-show-107- > applicability-machine-learning-networking/ > > [1] http://sebastianruder.com/transfer-learning/index.html > [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ > [3] https://openai.com/blog/ > [4] http://distill.pub/ > [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf > [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx > > --94eb2c1252bc9bb956054b6686d2 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Interestingly, Andrew also points out the need for data se= ts and the problem with talent pools (among many other things): =C2=A0https://hbr.org/2016/11/what-artificial-intelligence-can-and-can= t-do-right-now=C2=A0

--dmm


On Wed, Mar 22, = 2017 at 10:29 AM, David Meyer <dmm@1-4-5.net> wrote:
Folks,

I thought I'd try to get some discussion going by outlining some of m= y views as to why networking is lagging other areas in the development and = application of Machine Learning (ML). In particular, networking is way behi= nd what we might call the "perceptual tasks" (vision, NLP, roboti= cs, etc) as well as other areas (medicine, finance, ...). The attached slid= e from one of my decks tries to summarize the situation, but I'll give = a bit of an outline below.=C2=A0
<= br>
So why is networking lagging m= any other fields when it comes to the application of machine learning? Ther= e are several reasons which I'll try to outline here (I was fortunate e= nough to discuss this with the packetpushers crew a few weeks ago, see [0])= . These are in no particular order.

First, we don't have a = "useful" theory of networking (UTON). One way to think about what= such a theory would look like is by analogy to what we see with the succes= s of convolutional neural networks (CNNs) not only for vision but now for m= any other tasks. In that case there is a theory of how vision works, built = up from concepts like receptive fields, shared weights, simple and complex = cells, etc. For example, the input layer of a CNN isn't fully connected= ; rather connections reflect the receptive field of the input layer, which = is in a way that is "inspired" by biological vision (being very c= areful with "biological inspiration"). Same with the alternation = of convolutional and pooling layers; these loosely model the alternation of= simple and complex cells in the primary visual cortex (V1), the secondary = visual cortex(V2) and the Brodmann area (V3).=C2=A0BTW, such a theory seems= to be required for transfer learning [1], which we'll need if we don&#= 39;t want every network to be analyzed in an ad-hoc, one-off style (like we= see today).

The second thing that we need to think about is pu= blicly available standardized data sets. Examples here include MNIST, Image= Net, and many others. The result of having these data sets has been the ste= ady=C2=A0ratcheting down of error rates on tasks such as object and scene r= ecognition, NLP, and others to super-human levels. Suffice it to say we hav= e nothing like these data sets for networking. Networking data sets today a= re largely proprietary, and because there is no UTON, there is no real way = to compare results between them.
<= br>
Third, there is a large skill = set gap. Network engineers (us!) typically don't have the mathematical = background required to build effective machine learning at scale. See [2] f= or an outline of some of the mathematical skills that are essential for eff= ective ML. There is a lot more to this, involving how progress is made in M= L (open data, open source, open models, in general open science and associa= ted communities, see e.g., OpenAi [3], Distill [4], and many others). In an= y event we need build community and gain new skills if we want to be able t= o develop and apply state of the art machine learning algorithms to network= data, at scale. The bottom line is that it will be difficult if not imposs= ible to be effective in the ML space if we ourselves don't understand h= ow it works and further, if we can build explainable systems (noting that e= xplaining what the individual neurons in a deep neural network are doing is= notoriously difficult; that said much progress is being made). So we want = to build explainable, end-to-end trained systems, and to accomplish this we= ourselves need to understand how these algorithms work, but in training an= d in inference.

<= div>This email is already TL;DR but I'll add on= e more here: We need to learn control, not just prediction. Since we live i= n an inherently adversarial environment we need to take advantage of Reinfo= rcement Learning as well as the various attacks being formulated against ML= ; [5] gives one interesting example of attacks against policy networks usin= g adversarial examples. See also slides 31 and 32 of [6] for some more on t= his topic.

<= font color=3D"#000000">I hope some of this gets us thinking about the probl= ems we need to solve in order to be successful in the ML space. There's= plenty more of this on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.htm= l.
I'm looking forward to = the discussion.

<= div>Thanks,

--dmm





--94eb2c1252bc9bb956054b6686d2-- From nobody Thu Mar 23 11:00:45 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id A4665129AEB for ; Thu, 23 Mar 2017 11:00:43 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.748 X-Spam-Level: X-Spam-Status: No, score=-1.748 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_ENVFROM_END_DIGIT=0.25, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001, URIBL_BLOCKED=0.001] autolearn=no autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id ITqezoX3sL_m for ; Thu, 23 Mar 2017 11:00:40 -0700 (PDT) Received: from mail-qt0-x233.google.com (mail-qt0-x233.google.com [IPv6:2607:f8b0:400d:c0d::233]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 713A21294B8 for ; Thu, 23 Mar 2017 11:00:40 -0700 (PDT) Received: by mail-qt0-x233.google.com with SMTP id x35so182083251qtc.2 for ; Thu, 23 Mar 2017 11:00:40 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:in-reply-to:references:from:date:message-id:subject:to; bh=WIUTdJHGXEO/W482r43DsE2iwyEay+/jeEOPj1ZC0MM=; b=cxl75nZn+UwR5UBee6DUnFrsq4fmphcr0eXabnZJ+dmgXd8UbxBvG/CK/Bd914ZNTT 6ddXcA8XijUjNRf253U/KJ40ONrRmVFFWnOUwFLISZqDRfcPgqNcIdbpr2FxuHathtdq J+kNqurdiZQOe2bgsw9P/2hxgg1R29uwfFFSPFHtPdz7fnk81xLmpaWjVJ6LV6tyGOxk QlBe5b7t39UZC9fc4ldGj/eJjpkoEWia05zuqWT1UZ9GvpKeK6brOekdGpIEp/rIAB8p WBueeiz4zCCMdEDlwPPP+JT1/DOVj+vVnKGV7Mo3Te23m1rP7/GI08NqBRd2+Bnqcdwx GUNQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to; bh=WIUTdJHGXEO/W482r43DsE2iwyEay+/jeEOPj1ZC0MM=; b=NSYj5KyJj11vz00K74zG60kHsT+xSXLYle8XuUwWQzeVIr1M+ySB8uMxCCyBeW2cEq Umbpnrwj1REuSF9V1qVAUFZfBB/BlWuSObdpMhvK47LeJQiV7X1m+7SKvOIahT3rM/Nb KwiF6PVLlKT4wWzFdzyV3yvM8NJYn9jcjDOxUoHuBLcCs6BtnWfyao8nhl3zqVs4sCnl J5zvOoXEFU2IC9PPaqg3UQ/1WdEBlVGAgNzl7AqF6plWTBny7qDHXH6cqY+PNroBel2S AtBrhD8RQlggzPGwqHpwwHWXHvI1mhhtU/2Ghp64E1gduiEqdxhNaH7gEHbHbKL1Oh89 n4KQ== X-Gm-Message-State: AFeK/H0wj3//OGgXrkoRs6wK7yUD1LY4VpB6UTAT/xL3OukCaRvj7VXxbe1XHa4kps1s6HGr+gthB/guqShEHg== X-Received: by 10.237.37.71 with SMTP id w7mr3497413qtc.34.1490292039362; Thu, 23 Mar 2017 11:00:39 -0700 (PDT) MIME-Version: 1.0 Received: by 10.237.52.196 with HTTP; Thu, 23 Mar 2017 11:00:38 -0700 (PDT) In-Reply-To: References: From: Adeel Rehman Date: Thu, 23 Mar 2017 14:00:38 -0400 Message-ID: To: David Meyer , idnet@ietf.org Content-Type: multipart/alternative; boundary=001a114203d69e351f054b69a763 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Thu, 23 Mar 2017 18:00:43 -0000 --001a114203d69e351f054b69a763 Content-Type: text/plain; charset=UTF-8 Thank you David for your feedback :) Sorry i forgot to include distro initially. On Thu, Mar 23, 2017 at 1:40 PM, Adeel Rehman wrote: > Hi David > > > > You have point out excellent shortcomings of Machine learning applications > in Networking field. I have been chasing the same question for a while now. > > > I think part of the reason Network Packet data is very well > structured/designed as compare to other data sources (image, text etc). The > network data can be exploited using domain based algorithms very > effectively. Applying ML algorithm to learn the rules of network traffic is > somewhat costly compare to domain based algorithm. For e.g. > > > a. Learning shortest path, we have Dijkstra algorithm. Do we need > ML for this? > > > b. TCP optimization. We have 3 or 4 Optimization algorithms that are > very cost effective, run in client and server stack. Do we need ML for this? > > > c. There are ML papers that show HTTPS classification with good > accuracy using SVM, and Decision tree algorithms. But do we need these > algorithms? We can get classification using SSL SNI packet during SSL > handshake and that would be 100% accurate. > > Having said that I think there are areas where Machine learning can be > really helpful in detection non-structured behavior in Network Traffic > > a. Anomaly Detection and Threat prevention. There are several > algorithms out there but I think ML algorithms can outperform in this area. > I know one security vendor effectively uses ML to prevent DDOS attacks. > > b. Subscriber behavior, this is a hot topic for Telco operators. I > think unsupervised topic modeling ML method can provide grouping of > subscribers based on their usage. I actually have not seen any > vendor/operator doing it currently, may be my knowledge is limited. > > c. Self-orchestrated Network, this can be a big thing with NFV and > Cloud applications. ML algorithms can play a vital part here. I see Cognet > 5GPPP is taking initiative on this, but not much work from other vendors. > > and i apolo > > On Wed, Mar 22, 2017 at 1:29 PM, David Meyer wrote: > >> Folks, >> >> I thought I'd try to get some discussion going by outlining some of my >> views as to why networking is lagging other areas in the development and >> application of Machine Learning (ML). In particular, networking is way >> behind what we might call the "perceptual tasks" (vision, NLP, robotics, >> etc) as well as other areas (medicine, finance, ...). The attached slide >> from one of my decks tries to summarize the situation, but I'll give a bit >> of an outline below. >> >> So why is networking lagging many other fields when it comes to the >> application of machine learning? There are several reasons which I'll try >> to outline here (I was fortunate enough to discuss this with the >> packetpushers crew a few weeks ago, see [0]). These are in no particular >> order. >> >> First, we don't have a "useful" theory of networking (UTON). One way to >> think about what such a theory would look like is by analogy to what we see >> with the success of convolutional neural networks (CNNs) not only for >> vision but now for many other tasks. In that case there is a theory of how >> vision works, built up from concepts like receptive fields, shared weights, >> simple and complex cells, etc. For example, the input layer of a CNN isn't >> fully connected; rather connections reflect the receptive field of the >> input layer, which is in a way that is "inspired" by biological vision >> (being very careful with "biological inspiration"). Same with the >> alternation of convolutional and pooling layers; these loosely model the >> alternation of simple and complex cells in the primary visual cortex (V1), >> the secondary visual cortex(V2) and the Brodmann area (V3). BTW, such a >> theory seems to be required for transfer learning [1], which we'll need if >> we don't want every network to be analyzed in an ad-hoc, one-off style >> (like we see today). >> >> The second thing that we need to think about is publicly available >> standardized data sets. Examples here include MNIST, ImageNet, and many >> others. The result of having these data sets has been the steady ratcheting >> down of error rates on tasks such as object and scene recognition, NLP, and >> others to super-human levels. Suffice it to say we have nothing like these >> data sets for networking. Networking data sets today are largely >> proprietary, and because there is no UTON, there is no real way to compare >> results between them. >> >> Third, there is a large skill set gap. Network engineers (us!) typically >> don't have the mathematical background required to build effective machine >> learning at scale. See [2] for an outline of some of the mathematical >> skills that are essential for effective ML. There is a lot more to this, >> involving how progress is made in ML (open data, open source, open models, >> in general open science and associated communities, see e.g., OpenAi [3], >> Distill [4], and many others). In any event we need build community and >> gain new skills if we want to be able to develop and apply state of the art >> machine learning algorithms to network data, at scale. The bottom line is >> that it will be difficult if not impossible to be effective in the ML space >> if we ourselves don't understand how it works and further, if we can build >> explainable systems (noting that explaining what the individual neurons in >> a deep neural network are doing is notoriously difficult; that said much >> progress is being made). So we want to build explainable, end-to-end >> trained systems, and to accomplish this we ourselves need to understand how >> these algorithms work, but in training and in inference. >> >> This email is already TL;DR but I'll add one more here: We need to learn >> control, not just prediction. Since we live in an inherently adversarial >> environment we need to take advantage of Reinforcement Learning as well as >> the various attacks being formulated against ML; [5] gives one interesting >> example of attacks against policy networks using adversarial examples. See >> also slides 31 and 32 of [6] for some more on this topic. >> >> I hope some of this gets us thinking about the problems we need to solve >> in order to be successful in the ML space. There's plenty more of this on >> http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm/vita.html. >> I'm looking forward to the discussion. >> >> Thanks, >> >> --dmm >> >> >> >> >> [0] http://packetpushers.net/podcast/podcasts/pq-show-107-a >> pplicability-machine-learning-networking/ >> >> [1] http://sebastianruder.com/transfer-learning/index.html >> [2] http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/ >> [3] https://openai.com/blog/ >> [4] http://distill.pub/ >> [5] http://rll.berkeley.edu/adversarial/arXiv2017_AdversarialAttacks.pdf >> [6] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pptx >> >> >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> > > > -- > Syed Rehman > > -- Adeel Rehman 978-551-5511 --001a114203d69e351f054b69a763 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Thank you David for your feedback :)

So= rry i forgot to include distro initially.

On Thu, Mar 23, 2017 at 1:40 PM, Adeel = Rehman <adeelrehman85@gmail.com> wrote:

Hi David<= /span>

=C2=A0

You have point out excellent shortcomings of Machine learning applications in Networking field. I have been chasing the same question for a while now. =C2=A0

I think part of the reason Network Packet data is ve= ry well structured/designed as compare to other data sources (image, text etc). The= network data can be exploited using domain based algorithms very effectively. Apply= ing ML algorithm to learn the rules of network traffic is somewhat costly compa= re to domain based algorithm. For e.g.


a.=C2=A0=C2=A0=C2=A0=C2= =A0=C2=A0=C2=A0 Learning shortest path, we have Dijkstra algorithm. Do we need ML fo= r this?


b.=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 TCP optimization. We have 3 or 4 Optimization algorithms that are very cost effective, run in client and server stack. Do we need ML for this?


c.=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 There are ML papers that show HTTPS classification with good accuracy using SVM, and Decision tree algorithms. = But do we need these algorithms? We can get classification using SSL SNI packet during SSL handshake and that would be 100% accurate.

Having said that I think there are areas where Machi= ne learning can be really helpful in detection non-structured behavior in Netw= ork Traffic

a.=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0 Anomaly Detection and Threat prevention. There are several algorithms out there but I think ML algorithms can outperform i= n this area. I know one security vendor effectively uses ML to prevent DDOS a= ttacks.

b.=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0 Subscriber behavior, this is a hot topic for Telco operators. I think unsupervised topic modeling ML method can provide = grouping of subscribers based on their usage. I actually have not seen any vendor/op= erator doing it currently, may be my knowledge is limited.

c.=C2=A0=C2=A0=C2=A0= =C2=A0=C2=A0=C2=A0 Self-orchestrated Network, this can be a big thing with NFV and Cloud applications. ML algorithms can play a vital part = here. I see Cognet 5GPPP is taking initiative on this, but not much work from oth= er vendors.

and i apolo


On Wed, Mar 22, 2017 at 1:29 PM, David= Meyer <dmm@1-4-5.net> wrote:
Folks,

I thought I'd try to get some discussion going by outli= ning some of my views as to why networking is lagging other areas in the de= velopment and application of Machine Learning (ML). In particular, networki= ng is way behind what we might call the "perceptual tasks" (visio= n, NLP, robotics, etc) as well as other areas (medicine, finance, ...). The= attached slide from one of my decks tries to summarize the situation, but = I'll give a bit of an outline below.=C2=A0

So why is networ= king lagging many other fields when it comes to the application of machine = learning? There are several reasons which I'll try to outline here (I w= as fortunate enough to discuss this with the packetpushers crew a few weeks= ago, see [0]). These are in no particular order.

First, we don= 't have a "useful" theory of networking (UTON). One way to th= ink about what such a theory would look like is by analogy to what we see w= ith the success of convolutional neural networks (CNNs) not only for vision= but now for many other tasks. In that case there is a theory of how vision= works, built up from concepts like receptive fields, shared weights, simpl= e and complex cells, etc. For example, the input layer of a CNN isn't f= ully connected; rather connections reflect the receptive field of the input= layer, which is in a way that is "inspired" by biological vision= (being very careful with "biological inspiration"). Same with th= e alternation of convolutional and pooling layers; these loosely model the = alternation of simple and complex cells in the primary visual cortex (V1), = the secondary visual cortex(V2) and the Brodmann area (V3).=C2=A0BTW, such = a theory seems to be required for transfer learning [1], which we'll ne= ed if we don't want every network to be analyzed in an ad-hoc, one-off = style (like we see today).

The second thing that we need to thi= nk about is publicly available standardized data sets. Examples here includ= e MNIST, ImageNet, and many others. The result of having these data sets ha= s been the steady=C2=A0ratcheting down of error rates on tasks such as obje= ct and scene recognition, NLP, and others to super-human levels. Suffice it= to say we have nothing like these data sets for networking. Networking dat= a sets today are largely proprietary, and because there is no UTON, there i= s no real way to compare results between them.

Third, there is = a large skill set gap. Network engineers (us!) typically don't have the= mathematical background required to build effective machine learning at sc= ale. See [2] for an outline of some of the mathematical skills that are ess= ential for effective ML. There is a lot more to this, involving how progres= s is made in ML (open data, open source, open models, in general open scien= ce and associated communities, see e.g., OpenAi [3], Distill [4], and many = others). In any event we need build community and gain new skills if we wan= t to be able to develop and apply state of the art machine learning algorit= hms to network data, at scale. The bottom line is that it will be difficult= if not impossible to be effective in the ML space if we ourselves don'= t understand how it works and further, if we can build explainable systems = (noting that explaining what the individual neurons in a deep neural networ= k are doing is notoriously difficult; that said much progress is being made= ). So we want to build explainable, end-to-end trained systems, and to acco= mplish this we ourselves need to understand how these algorithms work, but = in training and in inference.

=
This email is already TL;DR but I= 'll add one more here: We need to learn control, not just prediction. S= ince we live in an inherently adversarial environment we need to take advan= tage of Reinforcement Learning as well as the various attacks being formula= ted against ML; [5] gives one interesting example of attacks against policy= networks using adversarial examples. See also slides 31 and 32 of [6] for = some more on this topic.

I hope some of this gets us thinking a= bout the problems we need to solve in order to be successful in the ML spac= e. There's plenty more of this on http://www.1-4-5.net/~dmm/ml and http://www.1-4-5.net/~dmm= /vita.html.
I'm looki= ng forward to the discussion.

=
Thanks,


_____________________________________= __________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet




--
Syed =C2=A0Rehman




--
Ad= eel Rehman
978-551-5511
--001a114203d69e351f054b69a763-- From nobody Thu Mar 23 18:35:07 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DAB0D129C20 for ; Thu, 23 Mar 2017 18:35:05 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.202 X-Spam-Level: X-Spam-Status: No, score=-4.202 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id VjgH5qjemaNd for ; Thu, 23 Mar 2017 18:35:03 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 5CB30129B10 for ; Thu, 23 Mar 2017 18:35:02 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v2O1Yxvb084798; Fri, 24 Mar 2017 10:34:59 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw2.nict.go.jp with ESMTP id v2O1YxgT084792; Fri, 24 Mar 2017 10:34:59 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id C02DDAB46; Fri, 24 Mar 2017 10:34:58 +0900 (JST) Date: Fri, 24 Mar 2017 10:35:04 +0900 From: Pedro Martinez-Julia To: David Meyer Cc: idnet@ietf.org Message-ID: <20170324013502.GA6088@spectre> References: MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 24 Mar 2017 01:35:06 -0000 Dear all, In order to keep track of the contribution and progress of our efforts I think we must have some collaboration platform. I have taken the liberty of creating a repository in GitHub [1], which can be shared easily, and provides both a wiki and issue tracker. For now I think that the content of this thread (beginning with David's email and replies) is a good starting point to write an initial "mission statement" that we can later evolve into the "IDNET: Architecture Design Principles" document or I-D. If you like this platform, please let me know and give me your username so I can give you write permissions so you can upload those documents if you like. If you do not want to upload by yourself, please give me your consent for me to upload both your text and the documents you shared, I will do it and you will be able to access it (r/o). Thank you very much. Regards, Pedro [1] https://github.com/pedromj/idnet -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Fri Mar 24 06:36:44 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id A9B4A129A16 for ; Fri, 24 Mar 2017 06:36:42 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id ZtMQy1TMKqkM for ; Fri, 24 Mar 2017 06:36:40 -0700 (PDT) Received: from mail-qt0-x22c.google.com (mail-qt0-x22c.google.com [IPv6:2607:f8b0:400d:c0d::22c]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 89C46129989 for ; Fri, 24 Mar 2017 06:36:40 -0700 (PDT) Received: by mail-qt0-x22c.google.com with SMTP id n21so2297705qta.1 for ; Fri, 24 Mar 2017 06:36:40 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=y6vNAgGsmgIRjDZMYgo8MrKnx3TZkIcr/Z8H5jq1Fm0=; b=ijIdhNmSiQK7m9surbCrP+Mk+8HzplYz7Jjw+XxmOoCGaW9cdxfSS/VvsMLvmMsJts 279nOVFG0cGoUZF/YLCjdwuyKPkcimJ7EX6mDp5XJsYVybjQV0VrvGizU7mjKtEdJyx9 ygl0UFyU/OC86Ux5Xl1h1tLxzRs6jDvdOqz+tnrN2PC5JoxiguG38VHVxfUKS+nTN5PK RDesZCbJRVoOIiRBtN0Xtun7dwFEG8oIA/FAfF26AQ+GOmYdkHMy5OApMPBvKBh8H0Ls pYT0D8jHpBzdB5lEvzn5P1Ib3SSorKlCxQfhzWp6uXwreCzOnYrEp9oBT8vYblMt8KUf rzdw== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=y6vNAgGsmgIRjDZMYgo8MrKnx3TZkIcr/Z8H5jq1Fm0=; b=dfZQjs6Db1tQQoGlDtenHeI5qccgTyEI/a62H38dXc87l/4PmWVYgd5l4n5hVa+kHb HdKHVQlFmxgSXiMBGnWlsN3836KXNwqjaX/JrK9F+pZHLulvELlznVtyE/Z/0OwiH8GS 9BENJ//7plJhuUe+lINVQYlbZANLuwZCekZM8F+ydAK+zu8l9VmndxBQH3KeVbORCsZ1 WGM3L61cUjiIzJWKxWIQjejWyTWwsNFd6OZxYk0P/ylVQHTZpBU/qwIj7NdZpI6ASO2j ggOArFlptfsABJeOG3Z0fe630SRdhgCAzQgBg3iGTA4XvGnDKHT4aFr+svbD41FhxKwe dfNw== X-Gm-Message-State: AFeK/H0N/50wbdo5NaOBVDrzSX/TRMNyVXQdfFjHs6tPfvDoSuy95zLwhDzoklxWZ7JDfM2cpfvWw/mp2jctLA== X-Received: by 10.200.55.57 with SMTP id o54mr8570561qtb.78.1490362599514; Fri, 24 Mar 2017 06:36:39 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Fri, 24 Mar 2017 06:36:38 -0700 (PDT) X-Originating-IP: [64.134.235.66] In-Reply-To: <20170324013502.GA6088@spectre> References: <20170324013502.GA6088@spectre> From: David Meyer Date: Fri, 24 Mar 2017 06:36:38 -0700 Message-ID: To: Pedro Martinez-Julia Cc: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a113c036e54d6c4054b7a1576 Archived-At: Subject: Re: [Idnet] A few ideas/suggestions to get us going X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 24 Mar 2017 13:36:43 -0000 --001a113c036e54d6c4054b7a1576 Content-Type: text/plain; charset=UTF-8 Thanks Pedro! --dmm On Thu, Mar 23, 2017 at 6:35 PM, Pedro Martinez-Julia wrote: > Dear all, > > In order to keep track of the contribution and progress of our efforts I > think we must have some collaboration platform. I have taken the liberty > of creating a repository in GitHub [1], which can be shared easily, and > provides both a wiki and issue tracker. > > For now I think that the content of this thread (beginning with David's > email and replies) is a good starting point to write an initial "mission > statement" that we can later evolve into the "IDNET: Architecture Design > Principles" document or I-D. > > If you like this platform, please let me know and give me your username > so I can give you write permissions so you can upload those documents if > you like. If you do not want to upload by yourself, please give me your > consent for me to upload both your text and the documents you shared, I > will do it and you will be able to access it (r/o). Thank you very much. > > Regards, > Pedro > > [1] https://github.com/pedromj/idnet > > -- > Pedro Martinez-Julia > Network Science and Convergence Device Technology Laboratory > Network System Research Institute > National Institute of Information and Communications Technology (NICT) > 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan > Email: pedro@nict.go.jp > --------------------------------------------------------- > *** Entia non sunt multiplicanda praeter necessitatem *** > --001a113c036e54d6c4054b7a1576 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Thanks Pedro!

--dmm


On Thu, Ma= r 23, 2017 at 6:35 PM, Pedro Martinez-Julia <pedro@nict.go.jp> wrote:
Dear all,

In order to keep track of the contribution and progress of our efforts I think we must have some collaboration platform. I have taken the liberty of creating a repository in GitHub [1], which can be shared easily, and
provides both a wiki and issue tracker.

For now I think that the content of this thread (beginning with David's=
email and replies) is a good starting point to write an initial "missi= on
statement" that we can later evolve into the "IDNET: Architecture= Design
Principles" document or I-D.

If you like this platform, please let me know and give me your username
so I can give you write permissions so you can upload those documents if you like. If you do not want to upload by yourself, please give me your
consent for me to upload both your text and the documents you shared, I
will do it and you will be able to access it (r/o). Thank you very much.
Regards,
Pedro

[1] https://github.com/pedromj/idnet

--
Pedro Martinez-Julia
Network Science and Convergence Device Technology Laboratory
Network System Research Institute
National Institute of Information and Communications Technology (NICT)
4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan
Email: pedro@nict.go.jp
---------------------------------------------------------
*** Entia non sunt multiplicanda praeter necessitatem ***

--001a113c036e54d6c4054b7a1576-- From nobody Fri Mar 24 06:54:30 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 4FF4012975C for ; Fri, 24 Mar 2017 06:54:29 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id cLro9WbvbqIi for ; Fri, 24 Mar 2017 06:54:27 -0700 (PDT) Received: from mail-qt0-x230.google.com (mail-qt0-x230.google.com [IPv6:2607:f8b0:400d:c0d::230]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 2F8C712971B for ; Fri, 24 Mar 2017 06:54:26 -0700 (PDT) Received: by mail-qt0-x230.google.com with SMTP id i34so2753283qtc.0 for ; Fri, 24 Mar 2017 06:54:26 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:from:date:message-id:subject:to; bh=LhF/Ini7HGMt00f++7IySPyXR6D2PRNGi7PWCz9iiB8=; b=Y9Bf07IGtdnlpwIm072fbCmIV6boltJQAKNTXzz5pEWM1J4ZA8EYsLPFAKSi/b1WxW kFJcfHN+2EY/3knRD44iYpz+utsP08aKn8OBaJ5EUnlOiKAxlZipKEk+x8kdeX3rMQHh soWIiLVqym6iPH4zMpesbO9z3e05jyWOfod9rbh5sFXzCt8d066d6XmyfOWWWitD8JEK +WP3eSfGlcWzZ5LbiWfz7bQuegobmCztk/YnySSsEJ2wQSNvYqzLzlD9eZ2jfR4RZ6Lg 1hZgfrnwkmxwO8R4e3taJaMwezP+8ytQde92fO3o9mOrdicrkly4eEBVcMOReILgw6hV sKHw== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:from:date:message-id:subject:to; bh=LhF/Ini7HGMt00f++7IySPyXR6D2PRNGi7PWCz9iiB8=; b=F6AZ0JLHikUtkaApLTXVpkomSJpkB9URDLI1ws/DFeYRppfC6JM+zckzS1mLmRg4Ko HT0XZ3Fci19oq853Dtk9aNM3nLo3eAxrFU1t9oDwF/lj6B9IXEHT+//uLFFPmhpbqPaQ 9OuPMJGrqJuJwKYEWfzoInOHBCUTQdstXtsT5Z8fpZ5exf5R3U1Bn2MqW4T0O4n2ktKG ne5jhZAlY91qiYH/iFvjGRQ1SJwI32h4hnEgCsakie70q+VHMLb7C+2D83hdp5356smD GJD6AePOZAlOveOmYJ4KayfBtEC+WeIpLUVLEszVXO+YhRVklmTuweRGIik7KVSXdUcw bxqw== X-Gm-Message-State: AFeK/H1Unzfg9uZEoRY6d/yI8iON3XiLK90SmWVkROGqYT6UBYV0006/1lExMT5jGoBCDHdB/Pq/fVE1OqgcGw== X-Received: by 10.200.47.9 with SMTP id j9mr8343183qta.57.1490363665582; Fri, 24 Mar 2017 06:54:25 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Fri, 24 Mar 2017 06:54:25 -0700 (PDT) X-Originating-IP: [64.134.235.66] From: David Meyer Date: Fri, 24 Mar 2017 06:54:25 -0700 Message-ID: To: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a113773b4dffdbf054b7a5452 Archived-At: Subject: [Idnet] Getting going with ML X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Fri, 24 Mar 2017 13:54:29 -0000 --001a113773b4dffdbf054b7a5452 Content-Type: text/plain; charset=UTF-8 Folks, If you are just getting going with ML there is a wealth of material to help you around the web. There are tons of tutorials, research papers, code, videos, ... to help. In addition, over the past couple of years I've written up tutorials for my teams as issues came up; see http://www.1-4-5.net/~dmm/ml; there are some intro documents here and pointers to the associated code (mostly but not all written in tensorflow). For example, if you are just getting going with Bayesian inference see http://www.1-4-5.net/~dmm/ml/ps.pdf, "Notes on some basic probability stuff", and/or http://www.1-4-5.net/~dmm/ml/vae.pdf on variational inference; variational inference is a form of approximate inference that, among other things, attempts to deal with the intractable integral in Bayes' Rule (the "evidence" aka "marginal likelihood") . There are also great curated lists of current papers all around the net, see e.g., https://github.com/terryum/awesome-deep-learning-papers (Most Cited Deep Learning Papers). To get going I also highly recommend Andrew Ng's Coursera ML course; most of the material is on youtube, e.g, https://www.youtube.com/watch?v=LLx4diIP83I (this one is on Logistic Regression, but the whole set is there) if you want to skip the Coursera overhead. Finally, there is a lot more material on http://www.1-4-5.net/~dmm/vita.html (under Recent Talks) and in https://github.com/davidmeyer/ml. As always, questions/comments/* greatly appreciated. --dmm --001a113773b4dffdbf054b7a5452 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

Folks,

If you are just getting going with ML there is a wealth of material = to help you around the web. There are tons of tutorials, research papers, c= ode, videos, ... to help. In addition, over the past couple of years I'= ve written up tutorials for my teams as issues came up; see http://www.1-4-5.net/~dmm/ml; there are some intro documents here and pointers to the associated code = (mostly but not all written in tensorflow).=C2=A0 For example, if you are j= ust getting going with Bayesian inference see=C2=A0http://www.1-4-5.net/~dmm/ml= /ps.pdf, "Notes on some basic probability stuff", =C2=A0and/o= r http://www.1-4-5.net/~dm= m/ml/vae.pdf on variational inference; variational inference is a form = of approximate inference that, among other things, attempts to deal with th= e intractable integral in Bayes' Rule (the "evidence" aka &qu= ot;marginal likelihood") . There are also great curated lists of curre= nt papers all around the net, see e.g.,=C2=A0https://github.com/terryum/awesome-de= ep-learning-papers (Most Cited Deep Learning Papers).=C2=A0

To get going I al= so highly recommend Andrew Ng's Coursera ML course; most of the materia= l is on youtube, e.g,=C2=A0https://www.youtube.com/watch?v=3DLLx4diIP83I (this one is on= Logistic Regression, but the whole set is there) if you want to skip the C= oursera overhead. Finally, there is a lot more material on http://www.1-4-5.net/~dmm/vita.html (un= der Recent Talks) and in=C2=A0= https://github.com/davidmeyer/ml.

<= /div>
As always, questions/comments/* greatly app= reciated.

--dmm

--001a113773b4dffdbf054b7a5452-- From nobody Mon Mar 27 18:52:43 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 9ACEA12984C for ; Mon, 27 Mar 2017 18:52:41 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.902 X-Spam-Level: X-Spam-Status: No, score=-1.902 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 3pqsWjdjlI3i for ; Mon, 27 Mar 2017 18:52:39 -0700 (PDT) Received: from ns1.nict.go.jp (ns1.nict.go.jp [IPv6:2001:df0:232:300::1]) by ietfa.amsl.com (Postfix) with ESMTP id 66F31129850 for ; Mon, 27 Mar 2017 18:52:39 -0700 (PDT) Received: from gw1.nict.go.jp (gw1.nict.go.jp [133.243.18.250]) by ns1.nict.go.jp with ESMTP id v2S1qcw7045427 for ; Tue, 28 Mar 2017 10:52:38 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw1.nict.go.jp with ESMTP id v2S1qcEI045423 for ; Tue, 28 Mar 2017 10:52:38 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id B671BAA92 for ; Tue, 28 Mar 2017 10:52:37 +0900 (JST) Date: Tue, 28 Mar 2017 10:52:31 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170328015230.GM4808@spectre> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith1 X-Virus-Status: Clean Archived-At: Subject: [Idnet] Informal Meeting X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 01:52:42 -0000 Dear all, It could be interesting for us to have an informal (lunch, coffee break) meeting to have some F2F discussion about the target of IDNET. If you agree, please propose a time-slot and place. Thank you very much. Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Tue Mar 28 09:30:13 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 48E20129461 for ; Tue, 28 Mar 2017 09:30:12 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.222 X-Spam-Level: X-Spam-Status: No, score=-4.222 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id fzdbvURAsxA0 for ; Tue, 28 Mar 2017 09:30:09 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id C75C2129405 for ; Tue, 28 Mar 2017 09:30:08 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml704-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DJV69821; Tue, 28 Mar 2017 16:30:06 +0000 (GMT) Received: from NKGEML413-HUB.china.huawei.com (10.98.56.74) by lhreml704-cah.china.huawei.com (10.201.108.45) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 28 Mar 2017 17:30:06 +0100 Received: from NKGEML515-MBS.china.huawei.com ([169.254.5.223]) by NKGEML413-HUB.china.huawei.com ([10.98.56.74]) with mapi id 14.03.0235.001; Wed, 29 Mar 2017 00:29:51 +0800 From: Sheng Jiang To: "idnet@ietf.org" Thread-Topic: Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4tQ== Date: Tue, 28 Mar 2017 16:29:50 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> Accept-Language: en-GB, zh-CN, en-US Content-Language: en-GB X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.152.58] Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020201.58DA8F8F.006A, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.5.223, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 32a569b00f35f30708f01b4bf17f8acd Archived-At: Subject: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 16:30:12 -0000 Hi, all,=0A= =0A= Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9. =0A= =0A= https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-def= ined-network-01.pdf=0A= =0A= Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may have an informal meeting to disc= uss some common interests and potential future activities (not any activiti= es in IETF, but also other STO or experimental trails, etc.) on Thursday m= orning.=0A= =0A= FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.=0A= =0A= https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844=0A= https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D510= 11=0A= =0A= Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.=0A= =0A= Best regards,=0A= =0A= Sheng= From nobody Tue Mar 28 09:45:08 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id C8F5E129405 for ; Tue, 28 Mar 2017 09:44:59 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -4.222 X-Spam-Level: X-Spam-Status: No, score=-4.222 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RCVD_IN_DNSWL_MED=-2.3, RCVD_IN_MSPIKE_H3=-0.01, RCVD_IN_MSPIKE_WL=-0.01, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 9OXAaZ5rkIXP for ; Tue, 28 Mar 2017 09:44:56 -0700 (PDT) Received: from lhrrgout.huawei.com (lhrrgout.huawei.com [194.213.3.17]) (using TLSv1 with cipher RC4-SHA (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 55CA9129449 for ; Tue, 28 Mar 2017 09:44:56 -0700 (PDT) Received: from 172.18.7.190 (EHLO lhreml702-cah.china.huawei.com) ([172.18.7.190]) by lhrrg01-dlp.huawei.com (MOS 4.3.7-GA FastPath queued) with ESMTP id DJV71531; Tue, 28 Mar 2017 16:44:54 +0000 (GMT) Received: from NKGEML413-HUB.china.huawei.com (10.98.56.74) by lhreml702-cah.china.huawei.com (10.201.108.43) with Microsoft SMTP Server (TLS) id 14.3.301.0; Tue, 28 Mar 2017 17:44:53 +0100 Received: from NKGEML515-MBS.china.huawei.com ([169.254.5.223]) by NKGEML413-HUB.china.huawei.com ([10.98.56.74]) with mapi id 14.03.0235.001; Wed, 29 Mar 2017 00:44:42 +0800 From: Sheng Jiang To: "idnet@ietf.org" Thread-Topic: Intelligence-Defined Network Architecture and Call for Interests Thread-Index: AQHSp96NotmNK6LAE0GQptHszQd4taGqdGuU Date: Tue, 28 Mar 2017 16:44:42 +0000 Message-ID: <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> Accept-Language: en-GB, zh-CN, en-US Content-Language: en-GB X-MS-Has-Attach: X-MS-TNEF-Correlator: x-originating-ip: [10.47.152.58] Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable MIME-Version: 1.0 X-CFilter-Loop: Reflected X-Mirapoint-Virus-RAPID-Raw: score=unknown(0), refid=str=0001.0A020206.58DA9306.0348, ss=1, re=0.000, recu=0.000, reip=0.000, cl=1, cld=1, fgs=0, ip=169.254.5.223, so=2013-06-18 04:22:30, dmn=2013-03-21 17:37:32 X-Mirapoint-Loop-Id: 01fc2218270f3d0aa986cb27c64e70fc Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 16:45:00 -0000 Oops... An important typo. Try again:=0A= =0A= we may have an informal meeting to discuss some common interests and potent= ial future activities (not "only" activities in IETF, but also other STO or= experimental trails, etc.) on Thursday morning.=0A= =0A= Sheng=0A= ________________________________________=0A= From: IDNET [idnet-bounces@ietf.org] on behalf of Sheng Jiang [jiangsheng@h= uawei.com]=0A= Sent: 29 March 2017 0:29=0A= To: idnet@ietf.org=0A= Subject: [Idnet] Intelligence-Defined Network Architecture and Call for Int= erests=0A= =0A= Hi, all,=0A= =0A= Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.=0A= =0A= https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-def= ined-network-01.pdf=0A= =0A= Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may have an informal meeting to disc= uss some common interests and potential future activities (not any activiti= es in IETF, but also other STO or experimental trails, etc.) on Thursday m= orning.=0A= =0A= FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.=0A= =0A= https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844=0A= https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D510= 11=0A= =0A= Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.=0A= =0A= Best regards,=0A= =0A= Sheng=0A= _______________________________________________=0A= IDNET mailing list=0A= IDNET@ietf.org=0A= https://www.ietf.org/mailman/listinfo/idnet=0A= From nobody Tue Mar 28 09:48:57 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id B697212944B for ; Tue, 28 Mar 2017 09:48:55 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id qPknlFp-nUUI for ; Tue, 28 Mar 2017 09:48:53 -0700 (PDT) Received: from mail-qk0-x241.google.com (mail-qk0-x241.google.com [IPv6:2607:f8b0:400d:c09::241]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 9D85912025C for ; Tue, 28 Mar 2017 09:48:53 -0700 (PDT) Received: by mail-qk0-x241.google.com with SMTP id r142so9460170qke.3 for ; Tue, 28 Mar 2017 09:48:53 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=jYh2QLvgYH/fUHuCyI+Zd0y8WjFK99j5VmSBwAxO8ro=; b=AYbhIxYNrauLe9SQHMSbe3njn5/QKYBS3NsFe9XoTJj66BNBf3eIAPoEo+C6HuyBVn o5PB7AcZUUdKeab2tn4eeQMKWYAaEDvfIg7jYrx1bze+EI2p9U49ospJ3p97Ydqgv9gS v+NRrBu17ZFIZ9omw+ELJ4mDDM4SuKG793G8Y48ej+B3ZYJRwfDjmRS6JC/MZ2pPOhZJ aC1c2D8AVb64+wFl4uUjuffQ13m3kSSDifDPOjVsYdWTtcLolvZDvUlJNZUbW3KJTADx UFt+2U85d0ah0Q0dHns5A+xMXosAQ4aj8Q/S8WYSsbjSo3YemMM+1npRTN9tmcWEMma4 2J1g== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=jYh2QLvgYH/fUHuCyI+Zd0y8WjFK99j5VmSBwAxO8ro=; b=Q9ehzl0EGnVXQIYcWVa91n/8X+ID71knmgyzJn8dCqvJjr5jlywal8VBwOUA2T2QT/ qnmgOWdfsCx3en4Am22Tipnvf07chNpUNayaXaVtU0ciTLrPZ13ATp5v6fvwvbNrNLuY QALpaP80MR0OTVP0x8gWwk//7fjbntrk5gVbuAQbLu5+CsR3T5IQO49GQHCsqapunu2r Ea0RMdNDpLcnzhz74PucP5BraaB0ETBtMMCgc1XQ3EfjxoOLDjQKxmBNk/n6gR7oOq+Q JIy+jIQYMgMU4vICATgZYTv6mzySb0+CsB/Snykhqzakh+ltm07QLCuvdbY1+WHr6tA7 uGHA== X-Gm-Message-State: AFeK/H3y8hplx73eYHzzc/i8nkIk2VFQP9Qpbx3eCDxtu13dIMNTkKtxIHbsvx53C1efi0nJ+uXpz/pbKUtegQ== X-Received: by 10.55.176.135 with SMTP id z129mr25300679qke.308.1490719732675; Tue, 28 Mar 2017 09:48:52 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 09:48:52 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> <5D36713D8A4E7348A7E10DF7437A4B927CD15A3C@NKGEML515-MBS.china.huawei.com> From: David Meyer Date: Tue, 28 Mar 2017 09:48:52 -0700 Message-ID: To: Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c06569420568b054bcd3c53 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 16:48:56 -0000 --94eb2c06569420568b054bcd3c53 Content-Type: text/plain; charset=UTF-8 Understand that it is an informal meeting, but any chance we can get a way to enable remote participation? Thanks, Dave On Tue, Mar 28, 2017 at 9:44 AM, Sheng Jiang wrote: > Oops... An important typo. Try again: > > we may have an informal meeting to discuss some common interests and > potential future activities (not "only" activities in IETF, but also other > STO or experimental trails, etc.) on Thursday morning. > > Sheng > ________________________________________ > From: IDNET [idnet-bounces@ietf.org] on behalf of Sheng Jiang [ > jiangsheng@huawei.com] > Sent: 29 March 2017 0:29 > To: idnet@ietf.org > Subject: [Idnet] Intelligence-Defined Network Architecture and Call for > Interests > > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, we > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 > https://portal.etsi.org/webapp/WorkProgram/Report_ > WorkItem.asp?WKI_ID=51011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --94eb2c06569420568b054bcd3c53 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Understand that it is an informal meeting, but any chance = we can get a way to enable remote participation?

Thanks,=

Dave


On Tue, Mar 28, 2017 at 9:44 AM, Sh= eng Jiang <jiangsheng@huawei.com> wrote:
Oops... An important typo. Try again:

we may have an informal meeting to discuss some common interests and potent= ial future activities (not "only" activities in IETF, but also ot= her STO or experimental trails, etc.)=C2=A0 on Thursday morning.

Sheng
________________________________________
From: IDNET [idnet-bounces@ietf.o= rg] on behalf of Sheng Jiang [= jiangsheng@huawei.com]
Sent: 29 March 2017 0:29
To: idnet@ietf.org
Subject: [Idnet] Intelligence-Defined Network Architecture and Call for Int= erests

Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com .= Then we may have an informal meeting to discuss some common interests and = potential future activities (not any activities in IETF, but also other STO= or experimental trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

_______________________________________________
IDNET mailing list
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--94eb2c06569420568b054bcd3c53-- From nobody Tue Mar 28 09:52:56 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 32752129569 for ; Tue, 28 Mar 2017 09:52:48 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -0.402 X-Spam-Level: X-Spam-Status: No, score=-0.402 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, RCVD_IN_DNSWL_NONE=-0.0001, RCVD_IN_SORBS_WEB=1.5, SPF_HELO_PASS=-0.001, SPF_PASS=-0.001] autolearn=no autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=aricent.onmicrosoft.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id TBuWBwEQLy9t for ; Tue, 28 Mar 2017 09:52:46 -0700 (PDT) Received: from IND01-MA1-obe.outbound.protection.outlook.com (mail-ma1ind01on0052.outbound.protection.outlook.com [104.47.100.52]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-SHA384 (256/256 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 81A1D1294D0 for ; 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dkim=none (message not signed) header.d=none;huawei.com; dmarc=none action=none header.from=aricent.com; x-originating-ip: [122.162.224.120] x-microsoft-exchange-diagnostics: 1; MAXPR01MB0107; 7:DOEfRgdrLEm7OmULt6px63rPQv5LUixJZ3TcbNmz4oeE+HGz3+23gRXc0xVroWsMcIIoOokm1i5lNd6vPdWIwGQ7+nJ14hw+FFQGDJjEZ5jjX9KKL8Xl2wBS4O/dVH4dQvxDcrwlJk5izGTOVFt0Gc7yLrVqSiJyM68FzFb3YZ6RjvyCREauvxVddjAcl5gH1hGgWn3rn41fgPFVxcg60y6rlChJ3h+EZpww7RLsqfUvdoioxUg6gnP7T1GTaD00L/ofPty9UKErR2Fw6nKtnomby6/U1bbjZ3I7HI0nOv3e0VEruOFSYNr3QmwPLK3JQTW7n+wMAaanmNTP4DKqKQ== x-ms-office365-filtering-correlation-id: 7862baf1-9212-463f-700b-08d475fad9ca x-ms-office365-filtering-ht: Tenant x-microsoft-antispam: UriScan:; BCL:0; PCL:0; RULEID:(22001)(2017030254075)(48565401081); SRVR:MAXPR01MB0107; x-microsoft-antispam-prvs: x-exchange-antispam-report-test: UriScan:(50582790962513); x-exchange-antispam-report-cfa-test: BCL:0; PCL:0; RULEID:(6040375)(2401047)(8121501046)(5005006)(3002001)(10201501046)(6055026)(6041248)(20161123562025)(20161123564025)(20161123560025)(20161123558025)(20161123555025)(6072148); 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Regards Shamik -----Original Message----- From: IDNET [mailto:idnet-bounces@ietf.org] On Behalf Of Sheng Jiang Sent: Tuesday, March 28, 2017 10:00 PM To: idnet@ietf.org Subject: [Idnet] Intelligence-Defined Network Architecture and Call for Int= erests Hi, all, Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9. https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-def= ined-network-01.pdf Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may have an informal meeting to disc= uss some common interests and potential future activities (not any activiti= es in IETF, but also other STO or experimental trails, etc.) on Thursday m= orning. FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below. https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D510= 11 Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area. Best regards, Sheng _______________________________________________ IDNET mailing list IDNET@ietf.org https://www.ietf.org/mailman/listinfo/idnet "DISCLAIMER: This message is proprietary to Aricent and is intended solely = for the use of the individual to whom it is addressed. It may contain privi= leged or confidential information and should not be circulated or used for = any purpose other than for what it is intended. If you have received this m= essage in error, please notify the originator immediately. If you are not t= he intended recipient, you are notified that you are strictly prohibited fr= om using, copying, altering, or disclosing the contents of this message. Ar= icent accepts no responsibility for loss or damage arising from the use of = the information transmitted by this email including damage from virus." From nobody Tue Mar 28 10:02:34 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 47E23129478 for ; Tue, 28 Mar 2017 10:02:33 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.7 X-Spam-Level: X-Spam-Status: No, score=-2.7 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (1024-bit key) header.d=unicauca.edu.co Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 9I7jxBwB0eCp for ; Tue, 28 Mar 2017 10:02:30 -0700 (PDT) Received: from mail-vk0-x22b.google.com (mail-vk0-x22b.google.com [IPv6:2607:f8b0:400c:c05::22b]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 0D0291292F4 for ; Tue, 28 Mar 2017 10:02:29 -0700 (PDT) Received: by mail-vk0-x22b.google.com with SMTP id d188so95462427vka.0 for ; Tue, 28 Mar 2017 10:02:29 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=unicauca.edu.co; s=google; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=//hmAoTZZRKA7Yt3vMb7gMZOWXX6ZnNxpiBjWwYWkBY=; b=V0XojgOqSSRrNgKBgH7UxTUo3TDc36xSykav+7CnCxnZGN1pBjLCSTlxICPA8C7Qfy 3Yi6+YAxC7P95GINImkmVQZIGk044rz1FvtFzR5uoVMMaz6N2Z9CqT6GtFXUslWAhThd hMMZVc7GzxedGgONWSUt0qy3h3d+CwnPSCFOI= X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=//hmAoTZZRKA7Yt3vMb7gMZOWXX6ZnNxpiBjWwYWkBY=; b=RXTdmqQf4i/DmfOtkRIHf2Ja2+nZMHHo4HOEhwb6rlGWptFSNxFsejhWukw4lTJc2b aOGgR+UBMrYo0ZHvMcOhinLBV0v+KuMjs6MLh/dIQm5Hs+F6J9QGzV9zInwaRNHVteTI VXV4wcqk0O4+LaN7Jw4pkHQYak7oJSqV7RSn7jX8vMjKF9Z9wRhDLG5V7/YeKc0Gwtur RB8ErgaFi8Hpdh1J/jK4Rgbk+x1D77j3PyevdLwZlteiHHhw7Io2cBZHle8Vjl20Z9eN vtf2mg5cYpT1YExnYyS29HVtJYhTN15KqD5zgDtiJFaFeIkUAi6XSMZOTSopiHGHy82i MltQ== X-Gm-Message-State: AFeK/H0cYLTOuUUByfpZKxq+UKrTr6+X/sZVtNjck6bsr42fqfr5IG7hQnC7x2qCYXWZdB6hlyOqxijzSPupYChPHxJ5h9HYzqJoel2CWm/FqwSgCPN9F48isJcv9Fm1eO5cb9I4xw== X-Received: by 10.31.21.201 with SMTP id 192mr12999238vkv.171.1490720548943; Tue, 28 Mar 2017 10:02:28 -0700 (PDT) MIME-Version: 1.0 Received: by 10.103.44.76 with HTTP; Tue, 28 Mar 2017 10:02:08 -0700 (PDT) In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: Oscar Mauricio Caicedo Rendon Date: Tue, 28 Mar 2017 12:02:08 -0500 Message-ID: To: Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a1143b56ec78fdf054bcd6c59 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 17:02:33 -0000 --001a1143b56ec78fdf054bcd6c59 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hi, all, If there is a meeting, I would like to participate remotely. Best regards, Oscar On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang wrote= : > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, w= e > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > https://portal.etsi.org/webapp/WorkProgram/Report_ > WorkItem.asp?WKI_ID=3D51011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --=20 *Oscar Mauricio Caicedo Rend=C3=B3n* *PhD Computer Science - Federal University of Rio Grande do Sul* *Full Profesor - University of Cauca* --=20 ------------------------------ *Universidad del Cauca: Comprometidos con la calidad.* --001a1143b56ec78fdf054bcd6c59 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hi, all,

If there is a meeting= , I would like to participate remotely.

Best regards,

<= /div>Oscar

On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang <jiangsheng@huawei.co= m> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com .= Then we may have an informal meeting to discuss some common interests and = potential future activities (not any activities in IETF, but also other STO= or experimental trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet



--
Oscar Mauric= io Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0Federal University of Rio Grande do Sul
Full Profesor - University of Cauca
<= /div>

= Universidad del Cauca: Comprometidos = con la calidad.
 --001a1143b56ec78fdf054bcd6c59-- From nobody Tue Mar 28 10:43:33 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 03C49129687 for ; Tue, 28 Mar 2017 10:43:32 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id 59BeXfdD7obV for ; Tue, 28 Mar 2017 10:43:29 -0700 (PDT) Received: from mail-qt0-x229.google.com (mail-qt0-x229.google.com [IPv6:2607:f8b0:400d:c0d::229]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 920C01293FC for ; 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Tue, 28 Mar 2017 10:43:28 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 10:43:27 -0700 (PDT) X-Originating-IP: [128.223.156.253] From: David Meyer Date: Tue, 28 Mar 2017 10:43:27 -0700 Message-ID: To: idnet@ietf.org Content-Type: multipart/alternative; boundary=001a11412d805f7c74054bcdff94 Archived-At: Subject: [Idnet] A short, useful and easy to read ML paper (if you haven't read it lately) X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 17:43:32 -0000 --001a11412d805f7c74054bcdff94 Content-Type: text/plain; charset=UTF-8 https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf --001a11412d805f7c74054bcdff94 Content-Type: text/html; charset=UTF-8 --001a11412d805f7c74054bcdff94-- From nobody Tue Mar 28 10:59:44 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 39CBB12778D for ; Tue, 28 Mar 2017 10:59:42 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id GeqtiRVSfmFO for ; Tue, 28 Mar 2017 10:59:39 -0700 (PDT) Received: from mail-qt0-x22d.google.com (mail-qt0-x22d.google.com [IPv6:2607:f8b0:400d:c0d::22d]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 971ED1293E8 for ; Tue, 28 Mar 2017 10:59:39 -0700 (PDT) Received: by mail-qt0-x22d.google.com with SMTP id x35so70914111qtc.2 for ; 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Tue, 28 Mar 2017 10:59:38 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 10:59:38 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: David Meyer Date: Tue, 28 Mar 2017 10:59:38 -0700 Message-ID: To: Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c0c995a34a6a6054bce3903 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 17:59:42 -0000 --94eb2c0c995a34a6a6054bce3903 Content-Type: text/plain; charset=UTF-8 Hey Sheng, I just wanted to revive my key concern on [0] (same one I made at the NMRL): The hard parts of getting Machine Learning intelligence into Networking is the Machine Learning part. In addition, successful deployment of ML requires knowledge of ML combined with domain knowledge. We definitely have the domain knowledge; the problem is that we don't have the ML knowledge, and this is one of the big factors holding us back; see e.g. Andrew's discussion of talent in [1]. Slides such as [0] seem to imply that *someone else* (in particular, not us) will handle the ML part of all of this. I'll just note that in general successful deployments of ML don't work this way; the domain experts will have to learn ML (and vice versa) for us to be successful (again, see [1] and many others). Perhaps a useful exercise would be to write an ID that makes your assumptions explicit? Thanks, Dave [0] https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-defined-network-01.pdf [1] https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang wrote: > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, we > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 > https://portal.etsi.org/webapp/WorkProgram/Report_ > WorkItem.asp?WKI_ID=51011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --94eb2c0c995a34a6a6054bce3903 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hey Sheng,

I just wanted to revive my k= ey concern on [0] (same one I made at the NMRL): The hard parts of getting = Machine Learning intelligence into Networking is the Machine Learning part.= In addition, successful deployment of ML requires knowledge of ML combined= with domain knowledge. We definitely have the domain knowledge; the proble= m is that we don't have the ML knowledge, and this is one of the big fa= ctors holding us back; see e.g. Andrew's discussion of talent in [1].= =C2=A0 Slides such as [0] seem to imply that *someone else* (in particular,= not us) =C2=A0will handle the ML part of all of this. I'll just note t= hat in general successful deployments of ML don't work this way; the do= main experts will have to learn ML (and vice versa) for us to be successful= (again, see [1] and many others).

Perhaps a usefu= l exercise would be to write an ID that makes your assumptions explicit?

Thanks,

Dave
=C2=A0


=

On Tue, Mar= 28, 2017 at 9:29 AM, Sheng Jiang <jiangsheng@huawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com .= Then we may have an informal meeting to discuss some common interests and = potential future activities (not any activities in IETF, but also other STO= or experimental trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet

--94eb2c0c995a34a6a6054bce3903-- From nobody Tue Mar 28 11:01:52 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id DB9B21293E8 for ; Tue, 28 Mar 2017 11:01:50 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.899 X-Spam-Level: X-Spam-Status: No, score=-1.899 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id tx3YWJizmdoh for ; Tue, 28 Mar 2017 11:01:48 -0700 (PDT) Received: from mail-qt0-x22b.google.com (mail-qt0-x22b.google.com [IPv6:2607:f8b0:400d:c0d::22b]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 82E181294D2 for ; Tue, 28 Mar 2017 11:01:48 -0700 (PDT) Received: by mail-qt0-x22b.google.com with SMTP id r45so71013065qte.3 for ; Tue, 28 Mar 2017 11:01:48 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=fmqYPB85VxhO+lvi/V/NE+flwOUvOuCSPyjBbShIWe4=; b=k9IS2usCmE1ydCMxR+HMjghqM6hpwGrPqzzmfdUvrDpguXr41OWvgezVm8eICkfbg6 2K4MXWz5TeM+JgP6AGZtofbHIRniTey3h3+dyTFkPZfC5fGayLLPcAbec19gqZ2xeL9R gB2EuD83O/YaqP5ZRiW5iJsI4SWKzFKi4XFu/SDWP4iRplM9v+DGS8s0v17YJWJQ/cZo tFTITFjdeefyUFkYXAHQrr3nNjRdY1YAQXH/ZPq8XbpID7In98nDxJ5J+fgGEUFtGQeb kYSoAVAZ6qAmNL6OcchYqIqcbd/40Au0BP7jCkV/fpXMggxt2T1Vd2pGAAPreGmqfq+Q 3afQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=fmqYPB85VxhO+lvi/V/NE+flwOUvOuCSPyjBbShIWe4=; b=MiiYRzCCGwbg6a90Pfw2KChtKCkZs2NcwRktmtYPjF2BzyrEO7Ivf3j4jHQvM6ED+3 i/G+3dT6atZk+WDt1YD9F4/ON8jqRsFbuAT/ucQ2ftPPyrsKaCh4de6B+7UYm3dDzZKV j4pSrYS55/KapZrPh9xP5UNRVGB1Fa4bDG4VyRFskDVUh7aThwTu76WG1kS8H16kNcKc ACkHldo8PlGMbEES+mqJDMR/3qxvDfmzxAQFExX9TRPdtM6hfL4vpcKKbKF4cEG3A2Ps 91gMg73e0hDxrDjB5TqfIgIPiOIDnEl1S/BK3HeFuT2fxh1cc5BXNiYEJZ5Gh38Ae7kQ eiaw== X-Gm-Message-State: AFeK/H2iqG4eXdOQ+uRQ2gY5A3kl68aSvoYBM0yn1n9xtKxNELzNC+GsxdiSb4YIZRT/CkTzVo0qQFv874X6BA== X-Received: by 10.237.38.229 with SMTP id q92mr27673117qtd.6.1490724107465; Tue, 28 Mar 2017 11:01:47 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 11:01:46 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: David Meyer Date: Tue, 28 Mar 2017 11:01:46 -0700 Message-ID: To: Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c0bb94ee265ff054bce40d3 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:01:51 -0000 --94eb2c0bb94ee265ff054bce40d3 Content-Type: text/plain; charset=UTF-8 s/NMRL/NMLRG/ (sorry about that). Dave On Tue, Mar 28, 2017 at 10:59 AM, David Meyer wrote: > Hey Sheng, > > I just wanted to revive my key concern on [0] (same one I made at the > NMRL): The hard parts of getting Machine Learning intelligence into > Networking is the Machine Learning part. In addition, successful deployment > of ML requires knowledge of ML combined with domain knowledge. We > definitely have the domain knowledge; the problem is that we don't have the > ML knowledge, and this is one of the big factors holding us back; see e.g. > Andrew's discussion of talent in [1]. Slides such as [0] seem to imply > that *someone else* (in particular, not us) will handle the ML part of all > of this. I'll just note that in general successful deployments of ML don't > work this way; the domain experts will have to learn ML (and vice versa) > for us to be successful (again, see [1] and many others). > > Perhaps a useful exercise would be to write an ID that makes your > assumptions explicit? > > Thanks, > > Dave > > > [0] https://www.ietf.org/proceedings/97/slides/slides- > 97-nmlrg-intelligence-defined-network-01.pdf > [1] https://hbr.org/2016/11/what-artificial-intelligence-can- > and-cant-do-right-now > > > On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang > wrote: > >> Hi, all, >> >> Although there are many understanding for Intelligence-Defined Network, >> we are actually using this IDN as a term reference to the SDN-beyond >> architecture that we presented in IETF97, see the below link. A reference >> model is presented in page 3, while potential standardization works is >> presented in page 9. >> >> https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg- >> intelligence-defined-network-01.pdf >> >> Although it might be a little bit too early for AI/ML in network giving >> the recent story of the concluded proposed NMLRG, we still would like to >> call for interests in IDN. Anybody (on site in Chicago this week) are >> interested in this or even wider topics regarding to AI/ML in network, >> please contact me on jiangsheng@huawei.com . Then we may have an >> informal meeting to discuss some common interests and potential future >> activities (not any activities in IETF, but also other STO or experimental >> trails, etc.) on Thursday morning. >> >> FYI, we have already working on a Work Item, called IDN in the ETSI NGP >> (Next Generation Protocol) ISG, links below. >> >> https://portal.etsi.org/tb.aspx?tbid=844&SubTB=844 >> https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem. >> asp?WKI_ID=51011 >> >> Meanwhile, please do use this mail list as a forum to discuss any topics >> that may applying AI/ML into network area. >> >> Best regards, >> >> Sheng >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> > > --94eb2c0bb94ee265ff054bce40d3 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
s/NMRL/NMLRG/ =C2=A0 (sorry about that). Dave

On Tue, Mar 28, 2017 at = 10:59 AM, David Meyer <dmm@1-4-5.net> wrote:
Hey Sheng,

I just wanted= to revive my key concern on [0] (same one I made at the NMRL): The hard pa= rts of getting Machine Learning intelligence into Networking is the Machine= Learning part. In addition, successful deployment of ML requires knowledge= of ML combined with domain knowledge. We definitely have the domain knowle= dge; the problem is that we don't have the ML knowledge, and this is on= e of the big factors holding us back; see e.g. Andrew's discussion of t= alent in [1].=C2=A0 Slides such as [0] seem to imply that *someone else* (i= n particular, not us) =C2=A0will handle the ML part of all of this. I'l= l just note that in general successful deployments of ML don't work thi= s way; the domain experts will have to learn ML (and vice versa) for us to = be successful (again, see [1] and many others).

Pe= rhaps a useful exercise would be to write an ID that makes your assumptions= explicit?

Thanks,

Dave
= =C2=A0


<= br>
On Tue, Mar 28, 2017 at 9:29 AM, Sheng Jiang = <jiangsheng@huawei.com> wrote:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https:= //www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence= -defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangshen= g@huawei.com . Then we may have an informal meeting to discuss some com= mon interests and potential future activities (not any activities in IETF, = but also other STO or experimental trails, etc.)=C2=A0 on Thursday morning.=

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.org/tb.aspx?tbid= =3D844&SubTB=3D844
https://portal.etsi.org= /webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


--94eb2c0bb94ee265ff054bce40d3-- From nobody Tue Mar 28 11:06:31 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 597AF1293F4 for ; Tue, 28 Mar 2017 11:06:30 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.999 X-Spam-Level: X-Spam-Status: No, score=-1.999 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_NONE=-0.0001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id XdLMzcAgcjWe for ; Tue, 28 Mar 2017 11:06:27 -0700 (PDT) Received: from mail-ot0-x232.google.com (mail-ot0-x232.google.com [IPv6:2607:f8b0:4003:c0f::232]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id BF81912778D for ; Tue, 28 Mar 2017 11:06:27 -0700 (PDT) Received: by mail-ot0-x232.google.com with SMTP id 102so47413971otv.0 for ; Tue, 28 Mar 2017 11:06:27 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:references:in-reply-to:from:date:message-id:subject:to :cc; bh=DDIXulQgcGqXxFI1W20r0zrEfPuLcEGIcxm+iCbmgzk=; b=gllUJZGXLuBqv7cpKAUy3GoFEIgtkHGYf+1wiFW+ECHHUPNRYrfWTOGeJUZjJc2h5V eMK4GzxM9Bf5aQXqsmVoazZ9nwNhJ3pvZ5bZPhVOamEgvUiKsyZObLZD7g49I7oemdxy JBrxdGUEb1phZ2ylYovdrP3DJDUcoUpFg7se5QpKmZQ/kv3rioAiw8tmsErWvnGqWxzE sh5kLTBd2n6dF0CSDttf8acNjnJpxUcElwIbFzRCP4NR2mTBtVVH2kt1VWMDQgqgks1g dmSCEnAIkqXgqIIvQ53lYHjYkM64eBvfo0NwlE8SRm0JD2eJ5WfDWvlQ299TAap3zbko pxxQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:references:in-reply-to:from:date :message-id:subject:to:cc; bh=DDIXulQgcGqXxFI1W20r0zrEfPuLcEGIcxm+iCbmgzk=; b=RKYM0zQB6Vm6jgqIyXlqpcnJ0kJxRj4+yr+pNNUuX41aQmknRMZC01vfIF0dqkrf5v 141mK44TZpbLb/DPYapopxn7kh2dXkJvl2IVdcyZBkSA5SAgKqMdh0cyi6xZ96zyL5pb yuZjlsQ8/rxVM0L18/lnTlLM3sexVq0q1TdMYZo+W4OirsN2oXYxzdMWl9sZdgBdxtkw 7a9J6AJjJlXgTXJhdGabqwnl1X8RCgaxa/uQNwZR3dCc7FM4FtY8wRE4IxxOFDQLQFW4 3i6Xu/4TAL06cpgbNSL4F/1a3NwsyH8FspkV6oZzdYJa7gfpYzlPkHGlnThIjLbjKV1n 3Jkg== X-Gm-Message-State: AFeK/H2VW6XESGXhyexu0ADliJYDydsdxOt/KNxSEHBhOhNerdxvxa8JHVDNiN7egtRSVTr4nAlr95Vu0dMMsw== X-Received: by 10.157.17.197 with SMTP id y5mr14543174oty.150.1490724386971; Tue, 28 Mar 2017 11:06:26 -0700 (PDT) MIME-Version: 1.0 References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: From: Brian Njenga Date: Tue, 28 Mar 2017 18:06:15 +0000 Message-ID: To: Oscar Mauricio Caicedo Rendon , Sheng Jiang Cc: "idnet@ietf.org" Content-Type: multipart/alternative; boundary=94eb2c1922128b2c3e054bce5171 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:06:30 -0000 --94eb2c1922128b2c3e054bce5171 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in the d= esign of the internet's architecture are continuing, albeit under a different name. I'd like to participate remotely as well. I have a question that someone more thoughtful=E2=80=8B than I could answer= : Are there research questions on how to anticipate network-threatening DDOS attacks, such as those coming from the Mirai botnet family, using ML? Even going as far as designing and standardising an efficient, secure network protocol for IoT devices. This is a complicated issue, which involves emerging markets, so I'm interested in useful ideas from any angle. Thanks, and I'm glad to meet you all. Best regards, Brian Muhia. On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, < omcaicedo@unicauca.edu.co> wrote: > Hi, all, > > If there is a meeting, I would like to participate remotely. > > Best regards, > > Oscar > > On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang > wrote: > > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, w= e > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-d= efined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D5= 1011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > -- > *Oscar Mauricio Caicedo Rend=C3=B3n* > *PhD Computer Science - Federal University of Rio Grande do Sul* > *Full Profesor - University of Cauca* > > ------------------------------ > *Universidad del Cauca: Comprometidos con la calidad.* > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > --=20 Some say he really tries to learn efficiently. --94eb2c1922128b2c3e054bce5171 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

I'm glad that the discussions=E2=80=8B on how to make AI= /ML useful in the design of the internet's architecture are continuing,= albeit under a different name. I'd like to participate remotely as wel= l.

I have a question that someone more thoughtful=E2=80=8B than= I could answer: Are there research questions on how to anticipate network-= threatening DDOS attacks, such as those coming from the Mirai botnet family= , using ML? Even going as far as designing and standardising an efficient, = secure network protocol for IoT devices. This is a complicated issue, which= involves emerging markets, so I'm interested in useful ideas from any = angle.

Thanks, and I'm glad to meet you all.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 = Oscar Mauricio Caicedo Rendon, <omcaicedo@unicauca.edu.co> wrote:
Hi, all,

If there is a meeting, I would like to p= articipate remotely.

B= est regards,

Oscar

On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang <jiangsheng@huawei.com> wrot= e:
Hi, all,=

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-in= telligence-defined-network-01.pdf

Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may have an informal meetin= g to discuss some common interests and potential future activities (not any= activities in IETF, but also other STO or experimental trails, etc.)=C2=A0= on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

https://portal.etsi.o= rg/tb.aspx?tbid=3D844&SubTB=3D844
htt= ps://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011<= /a>

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDN= ET@ietf.org
https://www.ietf.org/mailman/listinfo= /idnet



--
Oscar Maur= icio Caicedo Rend=C3=B3n
PhD Computer Science -=C2=A0Federal University of Rio Grande do Sul
=
Full Profesor - University = of Cauca

Universidad del Cauca: Comprometidos con la calidad.
_______________________________________= ________
IDNET mailing list
IDN= ET@ietf.org
https://www.ietf.org/mailman/listinfo= /idnet
--
Some say he really tries to learn efficientl= y.
--94eb2c1922128b2c3e054bce5171-- From nobody Tue Mar 28 11:17:26 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 0F52D1297C6 for ; Tue, 28 Mar 2017 11:17:23 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.599 X-Spam-Level: X-Spam-Status: No, score=-2.599 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=1-4-5-net.20150623.gappssmtp.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id zGJxFZsPc_ND for ; Tue, 28 Mar 2017 11:17:20 -0700 (PDT) Received: from mail-qk0-x22c.google.com (mail-qk0-x22c.google.com [IPv6:2607:f8b0:400d:c09::22c]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id DEDE81293F4 for ; Tue, 28 Mar 2017 11:17:19 -0700 (PDT) Received: by mail-qk0-x22c.google.com with SMTP id f11so72905730qkb.0 for ; Tue, 28 Mar 2017 11:17:19 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1-4-5-net.20150623.gappssmtp.com; s=20150623; h=mime-version:in-reply-to:references:from:date:message-id:subject:to :cc; bh=srL8eRogiNvVu/yzUxx5HJY3V7CyLPVF0D1/g74RN80=; b=ZCeoyAMyTe6/PiSw8nAF94pJ2yRJAM5gxedyr94z9wASNHO5gyx31lhXTLYct6B38k 5csIAtEe/3E4UbzzX+jB6qtTyQeh34tybxrgxVsXknyV6kkNgtTiXTS1dYgK6wM1XO47 Ad1yiDdxOmaTqnhYrICmqAOnqZcnfJA9/7F9+Q7A67fXrlrCgd8L/XOBiH70BTES4iM8 nYheFx1udQ3gADikhcfT5hAMtHl+yTrUJftlEHqFYRtDLs5mc26O/xGhQdd5vK2sdcm4 4P+h+TxadHC2wI9QPahbniOK1Uvw25PccRGCH4o597mt3W2pH2fE27OS/vsH8P8WrhPu U+iQ== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:in-reply-to:references:from:date :message-id:subject:to:cc; bh=srL8eRogiNvVu/yzUxx5HJY3V7CyLPVF0D1/g74RN80=; b=J7ew8y5PtmUByVekQ3iQsZMG720gMlRzAc59gWYXDCeI0XkoUBCOXNz7Zo6k+0KHTr IyPxodPPdZfGK/NzZNeYo46NmsXSI+J2nazUFiIfhsL8qZy8N/YBlA6W4Uxq33pdcJJw n54KMFBe7Vo8lMIIHuWMtpDm0Fa1BbmUQ5lBiL/PnuTAglKyezNPKD78uI7wvWO6MUvb nvlwXcrt1l248tmmtur8L4PsAfPNw2ZFmiDIu9Re19YmcZtIkcz6F/7JM7wuMIgvsrWp 4Id7AvinIYlkDsEp4Sv1fnX+sKVy8atq0LyZyjacuU2wRR1mEG/f4UClDG2kyb2wXHK/ UDag== X-Gm-Message-State: AFeK/H3jSoPT/GmFJ2gwcrIG7yJeeNLIG1GAgx0cw57XlwKsA0IOHu+/0OkJYWtV0Iy/c4yRBBmgUZ1cWDC9Jw== X-Received: by 10.55.87.198 with SMTP id l189mr25308401qkb.304.1490725038942; Tue, 28 Mar 2017 11:17:18 -0700 (PDT) MIME-Version: 1.0 Received: by 10.12.149.34 with HTTP; Tue, 28 Mar 2017 11:17:18 -0700 (PDT) X-Originating-IP: [128.223.156.253] In-Reply-To: References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> From: David Meyer Date: Tue, 28 Mar 2017 11:17:18 -0700 Message-ID: To: Brian Njenga Cc: Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a114dbb78678201054bce7839 Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:17:23 -0000 --001a114dbb78678201054bce7839 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Hey Brian, We are working on anomaly detection technologies to detect things like DDOS attacks; we're about to publish some results and will let you know when that is ready. In the mean time, you might want to look at attacks against ML itself, see [0], [1], and [2]. The bottom line here is that even simple linear models are susceptible to adversarial attacks, so things like the autoencoders we used to do binary classification (e.g., anomaly detection) are susceptible; see slides 16+ of [3]. Dave [0] https://arxiv.org/pdf/1312.6199.pdf [1] https://arxiv.org/pdf/1412.6572.pdf [2] https://arxiv.org/pdf/1602.02697.pdf [3] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pdf On Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga wrote: > I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in the= design > of the internet's architecture are continuing, albeit under a different > name. I'd like to participate remotely as well. > > I have a question that someone more thoughtful=E2=80=8B than I could answ= er: Are > there research questions on how to anticipate network-threatening DDOS > attacks, such as those coming from the Mirai botnet family, using ML? Eve= n > going as far as designing and standardising an efficient, secure network > protocol for IoT devices. This is a complicated issue, which involves > emerging markets, so I'm interested in useful ideas from any angle. > > Thanks, and I'm glad to meet you all. > > Best regards, > Brian Muhia. > > On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, < > omcaicedo@unicauca.edu.co> wrote: > >> Hi, all, >> >> If there is a meeting, I would like to participate remotely. >> >> Best regards, >> >> Oscar >> >> On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang >> wrote: >> >> Hi, all, >> >> Although there are many understanding for Intelligence-Defined Network, >> we are actually using this IDN as a term reference to the SDN-beyond >> architecture that we presented in IETF97, see the below link. A referenc= e >> model is presented in page 3, while potential standardization works is >> presented in page 9. >> >> https://www.ietf.org/proceedings/97/slides/slides- >> 97-nmlrg-intelligence-defined-network-01.pdf >> >> Although it might be a little bit too early for AI/ML in network giving >> the recent story of the concluded proposed NMLRG, we still would like to >> call for interests in IDN. Anybody (on site in Chicago this week) are >> interested in this or even wider topics regarding to AI/ML in network, >> please contact me on jiangsheng@huawei.com . Then we may have an >> informal meeting to discuss some common interests and potential future >> activities (not any activities in IETF, but also other STO or experiment= al >> trails, etc.) on Thursday morning. >> >> FYI, we have already working on a Work Item, called IDN in the ETSI NGP >> (Next Generation Protocol) ISG, links below. >> >> https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 >> https://portal.etsi.org/webapp/WorkProgram/Report_ >> WorkItem.asp?WKI_ID=3D51011 >> >> Meanwhile, please do use this mail list as a forum to discuss any topics >> that may applying AI/ML into network area. >> >> Best regards, >> >> Sheng >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> >> >> >> >> -- >> *Oscar Mauricio Caicedo Rend=C3=B3n* >> *PhD Computer Science - Federal University of Rio Grande do Sul* >> *Full Profesor - University of Cauca* >> >> ------------------------------ >> *Universidad del Cauca: Comprometidos con la calidad.* >> _______________________________________________ >> IDNET mailing list >> IDNET@ietf.org >> https://www.ietf.org/mailman/listinfo/idnet >> > -- > Some say he really tries to learn efficiently. > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > --001a114dbb78678201054bce7839 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable
Hey Brian,

We are working on anomaly de= tection technologies to detect things like DDOS attacks; we're about to= publish some results and will let you know when that is ready. In the mean= time, you might want to look at attacks against ML itself, see [0], [1], a= nd [2].=C2=A0 The bottom line here is that even simple linear models are=C2= =A0susceptible to adversarial attacks, so things like the autoencoders we u= sed to do binary classification (e.g., anomaly detection) are=C2=A0suscepti= ble; see slides 16+ of [3].

Dave

On Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga <iambrianmuhia@gmail.com> wrote:

I'm glad that the discussions=E2=80=8B on how t= o make AI/ML useful in the design of the internet's architecture are co= ntinuing, albeit under a different name. I'd like to participate remote= ly as well.

I have a question that someone more thoughtful=E2=80=8B than= I could answer: Are there research questions on how to anticipate network-= threatening DDOS attacks, such as those coming from the Mirai botnet family= , using ML? Even going as far as designing and standardising an efficient, = secure network protocol for IoT devices. This is a complicated issue, which= involves emerging markets, so I'm interested in useful ideas from any = angle.

Thanks, and I'm glad to meet you all.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 = Oscar Mauricio Caicedo Rendon, <omcaicedo@unicauca.edu.co> wrote:
= Hi, all,

If there is a meeting, I would like to partici= pate remotely.

Best regards,

Oscar<= br class=3D"m_3514546441752878514gmail_msg">

On Tue, Mar 28, 2017 at 1= 1:29 AM, Sheng Jiang <jiangsheng@huawei.com> wrot= e:
Hi, all,

Although there are many understanding for Intelligence-Defined Network, we = are actually using this IDN as a term reference to the SDN-beyond architect= ure that we presented in IETF97, see the below link. A reference model is p= resented in page 3, while potential standardization works is presented in p= age 9.

https://www.ietf.org/proceedings/97= /slides/slides-97-nmlrg-intelligence-defined-network-01.pdf
Although it might be a little bit too early for AI/ML in network giving the= recent story of the concluded proposed NMLRG, we still would like to call = for interests in IDN. Anybody (on site in Chicago this week) are interested= in this or even wider topics regarding to AI/ML in network, please contact= me on jiangsheng@huawei.com . Then we may hav= e an informal meeting to discuss some common interests and potential future= activities (not any activities in IETF, but also other STO or experimental= trails, etc.)=C2=A0 on Thursday morning.

FYI, we have already working on a Work Item, called IDN in the ETSI NGP (Ne= xt Generation Protocol) ISG, links below.

= https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844
https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011

Meanwhile, please do use this mail list as a forum to discuss any topics th= at may applying AI/ML into network area.

Best regards,

Sheng
_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf= .org/mailman/listinfo/idnet



--
Oscar Mauricio Caicedo Rend=C3=B3n
PhD Computer= Science -=C2=A0Federal University of Rio Grande do Sul
Full Profesor - University of Cauca
<= /div>

= Universidad del Cauca: Comprometidos con la calidad.=
_______________________________________________ IDNET mailing list
IDNET@ietf.org
https://www.ietf= .org/mailman/listinfo/idnet
--
Some say he really tries to lear= n efficiently.

_______________________________________________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet


--001a114dbb78678201054bce7839-- From nobody Tue Mar 28 11:25:46 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 2A9A1129893 for ; Tue, 28 Mar 2017 11:25:46 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -1.902 X-Spam-Level: X-Spam-Status: No, score=-1.902 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, RP_MATCHES_RCVD=-0.001, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id We5gLVdvNhM4 for ; Tue, 28 Mar 2017 11:25:44 -0700 (PDT) Received: from ns2.nict.go.jp (ns2.nict.go.jp [IPv6:2001:df0:232:300::2]) by ietfa.amsl.com (Postfix) with ESMTP id 5B9241299CD for ; Tue, 28 Mar 2017 11:25:38 -0700 (PDT) Received: from gw2.nict.go.jp (gw2.nict.go.jp [133.243.18.251]) by ns2.nict.go.jp with ESMTP id v2SIPbwg029333 for ; Wed, 29 Mar 2017 03:25:37 +0900 (JST) Received: from mail1.nict.go.jp (mail1.nict.go.jp [133.243.18.14]) by gw2.nict.go.jp with ESMTP id v2SIPbYp029270 for ; Wed, 29 Mar 2017 03:25:37 +0900 (JST) Received: from spectre (ssh1.nict.go.jp [133.243.3.49]) (using TLSv1.2 with cipher DHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (No client certificate requested) by mail1.nict.go.jp (NICT Mail Spool Server1) with ESMTPS id C2596A114 for ; Wed, 29 Mar 2017 03:25:36 +0900 (JST) Date: Wed, 29 Mar 2017 03:25:30 +0900 From: Pedro Martinez-Julia To: idnet@ietf.org Message-ID: <20170328182530.GP4808@spectre> References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline In-Reply-To: User-Agent: Mutt/1.8.0 (2017-02-23) X-Virus-Scanned: clamav-milter 0.98.7 at zenith2 X-Virus-Status: Clean Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:25:46 -0000 On Tue, Mar 28, 2017 at 10:59:38AM -0700, David Meyer wrote: > Hey Sheng, > > I just wanted to revive my key concern on [0] (same one I made at the > NMRL): The hard parts of getting Machine Learning intelligence into > Networking is the Machine Learning part. In addition, successful deployment > of ML requires knowledge of ML combined with domain knowledge. We > definitely have the domain knowledge; the problem is that we don't have the > ML knowledge, and this is one of the big factors holding us back; see e.g. > Andrew's discussion of talent in [1]. Slides such as [0] seem to imply > that *someone else* (in particular, not us) will handle the ML part of all > of this. I'll just note that in general successful deployments of ML don't > work this way; the domain experts will have to learn ML (and vice versa) > for us to be successful (again, see [1] and many others). Dear Dave, You are true in that ML/domain knowledge is necessary but, however it is worth to take into account that it is not strictly required and it will even be counterproductive in some (or maybe most) situations. At the end of the day, encouraging (or forcing) a network expert to learn ML is quite difficult, the results will be delayed until the learning phase ends, and (most probably) s/he will never get a better solution than a person that has been an expert in ML from a long time ago. Therefore, it is better to make separate experts (in ML and the domain itself) to collaborate in a common solution. Therefore, and I think it has been mentioned before, we have to (try to) enroll experts in ML to the IDNET group and see what can we do together... > Perhaps a useful exercise would be to write an ID that makes your > assumptions explicit? > > Thanks, > Dave Regards, Pedro -- Pedro Martinez-Julia Network Science and Convergence Device Technology Laboratory Network System Research Institute National Institute of Information and Communications Technology (NICT) 4-2-1, Nukui-Kitamachi, Koganei, Tokyo 184-8795, Japan Email: pedro@nict.go.jp --------------------------------------------------------- *** Entia non sunt multiplicanda praeter necessitatem *** From nobody Tue Mar 28 11:26:56 2017 Return-Path: X-Original-To: idnet@ietfa.amsl.com Delivered-To: idnet@ietfa.amsl.com Received: from localhost (localhost [127.0.0.1]) by ietfa.amsl.com (Postfix) with ESMTP id 61652128BBB for ; Tue, 28 Mar 2017 11:26:49 -0700 (PDT) X-Virus-Scanned: amavisd-new at amsl.com X-Spam-Flag: NO X-Spam-Score: -2.699 X-Spam-Level: X-Spam-Status: No, score=-2.699 tagged_above=-999 required=5 tests=[BAYES_00=-1.9, DKIM_SIGNED=0.1, DKIM_VALID=-0.1, DKIM_VALID_AU=-0.1, FREEMAIL_FROM=0.001, HTML_MESSAGE=0.001, RCVD_IN_DNSWL_LOW=-0.7, SPF_PASS=-0.001] autolearn=ham autolearn_force=no Authentication-Results: ietfa.amsl.com (amavisd-new); dkim=pass (2048-bit key) header.d=gmail.com Received: from mail.ietf.org ([4.31.198.44]) by localhost (ietfa.amsl.com [127.0.0.1]) (amavisd-new, port 10024) with ESMTP id kehU8U3bO48K for ; Tue, 28 Mar 2017 11:26:45 -0700 (PDT) Received: from mail-oi0-x230.google.com (mail-oi0-x230.google.com [IPv6:2607:f8b0:4003:c06::230]) (using TLSv1.2 with cipher ECDHE-RSA-AES128-GCM-SHA256 (128/128 bits)) (No client certificate requested) by ietfa.amsl.com (Postfix) with ESMTPS id 8BAF11293E8 for ; Tue, 28 Mar 2017 11:26:44 -0700 (PDT) Received: by mail-oi0-x230.google.com with SMTP id r203so44390723oib.3 for ; Tue, 28 Mar 2017 11:26:44 -0700 (PDT) DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=gmail.com; s=20161025; h=mime-version:references:in-reply-to:from:date:message-id:subject:to :cc; bh=HDT+C3XJRCBjC6CItCkpck+6Y1W+4UPsJM32HSjHtKI=; b=GvP4Z/5/MKGV65gecuYcTe5g506cfEhy8gmu3IWUvt8fWc58Yp1Xbx5GLM2SJ50nvZ c16RQ6ajHSupX/cUIQCJ4h6Qc8I9Mjmt92+0uE0zAfFfVxWzQjW8Rw6gveFBRYMzuqTS Znb0zQB3VU9Q2oZqTSCBytyBJpU3zUGsTbNLhSfmuUGmiHCFQU55r4rUza2H8xA13YiU u/B1xBEYErCTsPKMKnQGYdvTex4qLXhq205Ux7UFwimmahW+WSK4VWmRkNMTejXiyPfY THPyAJ7hfjK0EV1YzUtqTLtJvWaHEapPyIGJTGBuOBYxPNhRRLz0fc+SPiiYNtsZapSb yXSg== X-Google-DKIM-Signature: v=1; a=rsa-sha256; c=relaxed/relaxed; d=1e100.net; s=20161025; h=x-gm-message-state:mime-version:references:in-reply-to:from:date :message-id:subject:to:cc; bh=HDT+C3XJRCBjC6CItCkpck+6Y1W+4UPsJM32HSjHtKI=; b=pDuO9vB9SoyNSpvbts7uDToUDiU859sG+G7O3voCDfUMdtMMcDly/6lYjtWwdrcw2y U8FSVyaw89SJxVd66DFCFwFqOYKNYLKGW/Nr14sVknVewxY0ei3w0QisRUwV1zEDeR/N 1h7yFEK/amWoAEgTzWSzZ9oW8nvbA7BfWzEPHJZWT5kr4rInY1ztT5WguO23yCO3u7IH 4+7xje529wHz+qLfDK5iaV9J7Vzbll9L1qBtxgt5F5J+BlshMdunAcF51ytUbJ8rTIYn MgBYrnEf1wgonVfX/y2zYGyNVGTC62JOZPVd66QbcYcZ8O2cNtF3Ys4un1sKrLUNd7wI 7Dpg== X-Gm-Message-State: AFeK/H1NjD1XcNlnfy8d2U5WsNLu6js3LLzys1OCUwGI6mxMWrAedWcFtZCpst/P6PHiNWZI/EQwqBD6/ni6aQ== X-Received: by 10.202.207.85 with SMTP id f82mr16016365oig.159.1490725603862; Tue, 28 Mar 2017 11:26:43 -0700 (PDT) MIME-Version: 1.0 References: <5D36713D8A4E7348A7E10DF7437A4B927CD15A18@NKGEML515-MBS.china.huawei.com> In-Reply-To: From: Brian Njenga Date: Tue, 28 Mar 2017 18:26:33 +0000 Message-ID: To: David Meyer Cc: Oscar Mauricio Caicedo Rendon , Sheng Jiang , "idnet@ietf.org" Content-Type: multipart/alternative; boundary=001a113df38c1370b5054bce9adb Archived-At: Subject: Re: [Idnet] Intelligence-Defined Network Architecture and Call for Interests X-BeenThere: idnet@ietf.org X-Mailman-Version: 2.1.22 Precedence: list List-Id: "The IDNet \(Intelligence-Defined Network\) " List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Mar 2017 18:26:49 -0000 --001a113df38c1370b5054bce9adb Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Thanks, Dave. I'll check them out. Something I currently think is very cool is the Distill journal, at https://distill.pub, edited by Shan Carter and Chris Olah. I would love to see more ML tutorials=E2=80=8B and lessons written in that = style. They're=E2=80=8Baccepting submissions for tutorials, I think, so if any ML = expert here would like to explain any technique in an explorable manner, please reach out to them. On Tue, 28 Mar 2017, 21:17 David Meyer, wrote: > Hey Brian, > > We are working on anomaly detection technologies to detect things like > DDOS attacks; we're about to publish some results and will let you know > when that is ready. In the mean time, you might want to look at attacks > against ML itself, see [0], [1], and [2]. The bottom line here is that > even simple linear models are susceptible to adversarial attacks, so thin= gs > like the autoencoders we used to do binary classification (e.g., anomaly > detection) are susceptible; see slides 16+ of [3]. > > Dave > > [0] https://arxiv.org/pdf/1312.6199.pdf > [1] https://arxiv.org/pdf/1412.6572.pdf > [2] https://arxiv.org/pdf/1602.02697.pdf > [3] http://www.1-4-5.net/~dmm/ml/talks/2016/cor_ml4networking.pdf > > On Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga > wrote: > > I'm glad that the discussions=E2=80=8B on how to make AI/ML useful in the= design > of the internet's architecture are continuing, albeit under a different > name. I'd like to participate remotely as well. > > I have a question that someone more thoughtful=E2=80=8B than I could answ= er: Are > there research questions on how to anticipate network-threatening DDOS > attacks, such as those coming from the Mirai botnet family, using ML? Eve= n > going as far as designing and standardising an efficient, secure network > protocol for IoT devices. This is a complicated issue, which involves > emerging markets, so I'm interested in useful ideas from any angle. > > Thanks, and I'm glad to meet you all. > > Best regards, > Brian Muhia. > > On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Rendon, < > omcaicedo@unicauca.edu.co> wrote: > > Hi, all, > > If there is a meeting, I would like to participate remotely. > > Best regards, > > Oscar > > On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang > wrote: > > Hi, all, > > Although there are many understanding for Intelligence-Defined Network, w= e > are actually using this IDN as a term reference to the SDN-beyond > architecture that we presented in IETF97, see the below link. A reference > model is presented in page 3, while potential standardization works is > presented in page 9. > > > https://www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-d= efined-network-01.pdf > > Although it might be a little bit too early for AI/ML in network giving > the recent story of the concluded proposed NMLRG, we still would like to > call for interests in IDN. Anybody (on site in Chicago this week) are > interested in this or even wider topics regarding to AI/ML in network, > please contact me on jiangsheng@huawei.com . Then we may have an informal > meeting to discuss some common interests and potential future activities > (not any activities in IETF, but also other STO or experimental trails, > etc.) on Thursday morning. > > FYI, we have already working on a Work Item, called IDN in the ETSI NGP > (Next Generation Protocol) ISG, links below. > > https://portal.etsi.org/tb.aspx?tbid=3D844&SubTB=3D844 > https://portal.etsi.org/webapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D5= 1011 > > Meanwhile, please do use this mail list as a forum to discuss any topics > that may applying AI/ML into network area. > > Best regards, > > Sheng > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > > > -- > *Oscar Mauricio Caicedo Rend=C3=B3n* > *PhD Computer Science - Federal University of Rio Grande do Sul* > *Full Profesor - University of Cauca* > > ------------------------------ > *Universidad del Cauca: Comprometidos con la calidad.* > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > -- > Some say he really tries to learn efficiently. > > _______________________________________________ > IDNET mailing list > IDNET@ietf.org > https://www.ietf.org/mailman/listinfo/idnet > > > -- Some say he really tries to learn efficiently. --001a113df38c1370b5054bce9adb Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable

Thanks, Dave. I'll check them out.

Something I currently think is very cool is the Distill jour= nal, at https://distill.pub, edited by = Shan Carter and Chris Olah.

I would love to see more ML tutorials=E2=80=8B and lessons w= ritten in that style. They're=E2=80=8Baccepting submissions for tutoria= ls, I think, so if any ML expert here would like to explain any technique i= n an explorable manner, please reach out to them.


On Tue, 28 Mar 2017, 21:17 = David Meyer, <dmm@1-4-5.net> wro= te:
Hey Brian,

We are working on anomaly detection technologies to de= tect things like DDOS attacks; we're about to publish some results and = will let you know when that is ready. In the mean time, you might want to l= ook at attacks against ML itself, see [0], [1], and [2].=C2=A0 The bottom l= ine here is that even simple linear models are=C2=A0susceptible to adversar= ial attacks, so things like the autoencoders we used to do binary classific= ation (e.g., anomaly detection) are=C2=A0susceptible; see slides 16+ of [3]= .

Dave

[0]=C2=A0https://arxiv.org/pdf/1312.6= 199.pdf
[1]=C2=A0https://arxiv= .org/pdf/1412.6572.pdf
[2]=C2=A0https://arxiv.org/pdf/1602.02697.pdf
[3]=C2=A0http://www.1-4-5.net/~dmm/ml= /talks/2016/cor_ml4networking.pdf

On = Tue, Mar 28, 2017 at 11:06 AM, Brian Njenga <iambrianmuhia@gmail.com> wrote:

I'm glad that the discussions=E2=80=8B on how to make AI/ML= useful in the design of the internet's architecture are continuing, al= beit under a different name. I'd like to participate remotely as well. =

I have a question that someone more thou= ghtful=E2=80=8B than I could answer: Are there research questions on how to= anticipate network-threatening DDOS attacks, such as those coming from the= Mirai botnet family, using ML? Even going as far as designing and standard= ising an efficient, secure network protocol for IoT devices. This is a comp= licated issue, which involves emerging markets, so I'm interested in us= eful ideas from any angle.

Thanks, and I'm glad to meet you al= l.

Best regards,
Brian Muhia.


On Tue, 28 Mar 2017, 20:02 Oscar Mauricio Caicedo Re= ndon, <omcaicedo@unicauca.edu.co> wrote:
=
Hi, all,

If there is a meeting, I would like to participate rem= otely.

Best regards,

Oscar

On Tue, Mar 28, 2017 at 11:29 AM, Sheng Jiang= <jiangsheng@huawei.com> wrote:
Hi, all,=

https://= www.ietf.org/proceedings/97/slides/slides-97-nmlrg-intelligence-defined-net= work-01.pdf

jiangsheng@= huawei.com . Then we may have an informal meeting to discuss some commo= n interests and potential future activities (not any activities in IETF, bu= t also other STO or experimental trails, etc.)=C2=A0 on Thursday morning.

https://portal.etsi.org/tb.aspx?tbid=3D844&= amp;SubTB=3D844
https://portal.etsi.org/w= ebapp/WorkProgram/Report_WorkItem.asp?WKI_ID=3D51011



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--
Oscar Mauricio Caicedo Rend=C3=B3n
PhD = Computer Science -=C2=A0Federal= University of Rio Grande do Sul
Full Profesor - Univer= sity of Cauca

= Universidad del Cauca: Comprometidos con la calidad.
____________________________________= ___________
IDNET mailing list
IDNET@ietf.org
https://www.ietf.org/mailman/listinfo/idnet
--
Some say he really tries to= learn efficiently.

_______________________________________________
IDNET mailing list
IDN= ET@ietf.org
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