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

doi:10.1007/s10586-022-03709-w

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

2022-08-25

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DOI：10.1007/s10586-022-03709-w

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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.

Keywords:

Encrypted traffic classification

Feature fusion

Path signature

Machine learning

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

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