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ACER: detecting Shadowsocks server based on active probe technology

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Abstract

Anonymous server is created for hiding the information of hosts when they are surfing the Internet, such as Tor, Shadowsocks, etc. It is quite difficult to identify these servers, which provides potential criminals with opportunities to commit crime. Also, hackers can make use of these servers to threaten public network security, such as DDoS and Phishing attacks. Hence, the study of identifying these servers is pretty crucial. Current works on detecting Shadowsocks servers are mostly based on the features of servers’ data stream combined with machine learning. However, they are passive methods because they can only be established when the servers are in connection state. Therefore, we propose a new system named ACER, which AC means active and ER means expert, to detect these servers. Besides, we introduce XGBoost algorithm to process the data stream to optimize the detection. The method can recognize more Shadowsocks servers actively instead of monitoring the communication tunnel passively to identify the servers. The experiment result has achieved an accuracy of 94.63% by taking proposed framework and 1.20% more accurate than other existing solutions. We hope to provide a novel solution for those who are conducting research in this area, and provide a detection scheme for network censors to block illegal servers at the same time.

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Notes

  1. Great Firewall, https://en.wikipedia.org/ wiki/Great_Firewall.

  2. Free-ss.site, https://free-ss.site.

  3. FOFA, https://fofa.so/.

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Acknowledgements

We thank anonymous reviewers for provided helpful comments on earlier drafts of the manuscript. This work is partly supported by Development Plan Project of Sichuan Province (No. 20ZDYF3077) and Key Lab of Information Network Security, Ministry of Public Security (C19601).

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Correspondence to Tao Zhang.

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Cheng, J., Li, Y., Huang, C. et al. ACER: detecting Shadowsocks server based on active probe technology. J Comput Virol Hack Tech 16, 217–227 (2020). https://doi.org/10.1007/s11416-020-00353-z

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