Re: [Idnet] A few ideas/suggestions to get us going
David Meyer <dmm@1-4-5.net> Wed, 29 March 2017 19:15 UTC
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From: David Meyer <dmm@1-4-5.net>
Date: Wed, 29 Mar 2017 12:15:01 -0700
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To: "João Paulo S. Medeiros" <jpsm1985@gmail.com>
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Subject: Re: [Idnet] A few ideas/suggestions to get us going
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Hey João, 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, João Paulo S. Medeiros <jpsm1985@gmail.com > 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 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. João Paulo Souza Medeiros > > 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 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 >> >> >
- [Idnet] A few ideas/suggestions to get us going David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… Rana Pratap Sircar
- Re: [Idnet] A few ideas/suggestions to get us goi… Henk Birkholz
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… Adeel Rehman
- Re: [Idnet] A few ideas/suggestions to get us goi… Pedro Martinez-Julia
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- Re: [Idnet] A few ideas/suggestions to get us goi… João Paulo S. Medeiros
- Re: [Idnet] A few ideas/suggestions to get us goi… Pedro Martinez-Julia
- Re: [Idnet] A few ideas/suggestions to get us goi… João Paulo S. Medeiros
- Re: [Idnet] A few ideas/suggestions to get us goi… David Meyer
- [Idnet] FPS game traffic datasets... Re: A few id… grenville armitage