Seeing Traffic Paths: Encrypted Traffic Classification With Path Signature Features

Abstract

Although many network traffic protection methods have been developed to protect user privacy, encrypted traffic can still reveal sensitive user information with sophisticated analysis. In this paper, we propose ETC-PS, a novel encrypted traffic classification method with path signature. We first construct the traffic path with a session packet length sequence to represent the interactions between the client and the server. Then, path transformations are conducted to exhibit its structure and obtain different information. A multiscale path signature is finally computed as a kind of distinctive feature to train the traditional machine learning classifier, which achieves highly robust accuracy and low training overhead. Six publicly available datasets with different traffic types of HTTPS/1, HTTPS/2, QUIC, VPN, non-VPN, Tor, and non-Tor are used to conduct closed-world and open-world evaluations to verify the effectiveness of ETC-PS. The experimental results demonstrate that ETC-PS is superior to the state-of-the-art methods in terms of accuracy, f1 score, time complexity, and stability.