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] (see also [8] and [9], under Recent Talks).
[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
[8] http://www.1-4-5.net/~dmm/ml/talks/2017/nanog61.pptx
[9] http://www.1-4-5.net/~dmm/vita.hml