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The increasing success of Machine Learning (ML) and Deep Learning (DL) hasrecently re-sparked interest towards traffic classification. Whileclassification of known traffic is a well investigated subject with supervisedclassification tools (such as ML and DL models) are known to providesatisfactory performance, detection of unknown (or zero-day) traffic is morechallenging and typically handled by unsupervised techniques (such asclustering algorithms).  In this paper, we share our experience on a commercial-grade DL trafficclassification engine that is able to (i) identify known applications fromencrypted traffic, as well as (ii) handle unknown zero-day applications. Inparticular, our contribution for (i) is to perform a thorough assessment ofstate of the art traffic classifiers in commercial-grade settings comprisingfew thousands of very fine grained application labels, as opposite to the fewtens of classes generally targeted in academic evaluations. Additionally, wecontribute to the problem of (ii) detection of zero-day applications byproposing a novel technique, tailored for DL models, that is significantly moreaccurate and light-weight than the state of the art.  Summarizing our main findings, we gather that (i) while ML and DL models areboth equally able to provide satisfactory solution for classification of knowntraffic, however (ii) the non-linear feature extraction process of the DLbackbone provides sizeable advantages for the detection of unknown classes.

Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications