Deep Learning and Zero-Day Traffic Classification: Lessons Learned From a Commercial-Grade Dataset

The increasing success of Machine Learning (ML) and Deep Learning (DL) has recently re-sparked interest towards traffic classification. While supervised techniques provide satisfactory performance when classifying known traffic, the detection of zero-day (i.e., unknown) traffic is a more challenging task. At the same time, zero-day detection, generally tackled with unsupervised techniques such as clustering, received less coverage by the traffic classification literature which focuses more on deriving DL models via supervised techniques. Moreover, the combination of supervised and unsupervised techniques poses challenges not fully covered by the traffic classification literature. In this paper, we share our experience on a commercial-grade DL traffic classification engine that combines supervised and unsupervised techniques to identify known and zero-day traffic. In particular, we rely on a dataset with hundreds of very fine grained application labels, and perform a thorough assessment of two state of the art traffic classifiers in commercial-grade settings. This pushes the boundaries of traffic classifiers evaluation beyond the few tens of classes typically used in the literature. Our main contribution is the design and evaluation of GradBP, a novel technique for zero-day applications detection. Based on gradient backpropagation and tailored for DL models, GradBP yields superior performance with respect to state of the art alternatives, in both accuracy and computational cost. Overall, while ML and DL models are both equally able to provide excellent performance for the classification of known traffic, the non-linear feature extraction process of DL models backbone provides sizable advantages for the detection of unknown classes over classical ML models.