Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (14): 110-115.DOI: 10.3778/j.issn.1002-8331.2005-0161

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Tor Anonymous Traffic Identification Based on Histogram-XGBoost

WANG Tengfei, CAI Manchun, YUE Ting, LU Tianliang   

  1. School of Police Information Engineering and Cyber Security, People’s Public Security University of China, Beijing 100076, China
  • Online:2021-07-15 Published:2021-07-14



  1. 中国人民公安大学 警务信息工程与网络安全学院,北京 100076


Anonymous communication network is becoming a hidden space for criminals, which brings serious challenges to network supervision. The effective identification of anonymous network traffic is a prerequisite for its effective supervision. In terms of Tor anonymous traffic, Histogram-XGBoost, an effective traffic identification model is proposed. The Histogram-XGBoost model calculates the time-dependent features of the traffic on the flow granularity, and then performs discretization-like preprocessing on these features to improve the robustness. Finally, combined with the idea of integrated learning, the model realizes the identification of Tor anonymous traffic in the smaller feature dimension by XGBoost. Experimental results show that compared with the existing recognition methods, the model proposed in this paper has a greater improvement in accuracy and stability.

Key words: anonymous network, Tor, traffic identification, XGBoost



关键词: 匿名网络, Tor, 流量识别, XGBoost