Seq2Path: a sequence-to-path-based flow feature fusion approach for encrypted traffic classification

With the increasing awareness of user privacy protection and communication security, encrypted traffic has increased dramatically. Usually utilizing the flow information of the traffic, flow statistics-based methods are able to classify encrypted traffic. However, these methods require a large number of packets and manual selection of statistical features. In this paper, we propose a novel encrypted traffic classification method (Seq2Path), which fuses flow features by using path signature theory to translate feature sequences into a traffic path. Then, the statistical features of the traffic path are generated by computing its signature; and finally, these features are used to train a machine learning classifier. Our experiments on four datasets containing three types of traffic (HTTPS, VPN and Tor) show that Seq2Path achieves stable performance and generally outperforms state-of-the-art methods.