Shadowsocks account free12/17/2022 He, G., Yang, M., Gu, X., Luo, J., Ma, Y.: A novel active website fingerprinting attack against tor anonymous system. In: Proceedings of the 12th International Conference on Availability, Reliability and Security, pp. Hodo, E., Bellekens, X., Iorkyase, E., Hamilton, A., Tachtatzis, C., Atkinson, R.: Machine learning approach for detection of nontor traffic. R News 2(3), 18–22 (2002)ĭingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. In: Annual Meeting of the Society for Academic Emergency Medicine in San Francisco, CA, vol. Lewis, R.J: An introduction to classification and regression tree (cart) analysis. IEEE Access 7, 41017–41032 (2019)Īmari, S., et al.: The Handbook of Brain Theory and Neural Networks. Zeng, X., Chen, X., Shao, G., He, T., Han, Z., Wen, Y., Wang, Q.: Flow context and host behavior based Shadowsocks’s traffic identification. In: 9th International Conference on Intelligent Human–Machine Systems and Cybernetics (IHMSC), vol. IEEE (2016)ĭeng, Z., Liu, Z., Chen, Z., Guo, Y.: The random forest based detection of shadowsock’s traffic. In: IEEE International Conference on Cybercrime and Computer Forensic (ICCCF), pp. Pannu, M., Gill, B., Bird, R., Yang, K., Farrel, B.: Exploring proxy detection methodology. 8–14 (2017)ĭixon, L., Ristenpart, T., Shrimpton, T.: Network traffic obfuscation and automated internet censorship. In: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Industrial Track, pp. Lu, Z., Li, Z., Yang, J., Xu, T., Zhai, E., Liu, Y., Wilson, C.: Accessing google scholar under extreme internet censorship: a legal avenue. 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. The experiment result has achieved an accuracy of 94.63% by taking proposed framework and 1.20% more accurate than other existing solutions. The method can recognize more Shadowsocks servers actively instead of monitoring the communication tunnel passively to identify the servers. Besides, we introduce XGBoost algorithm to process the data stream to optimize the detection. Therefore, we propose a new system named ACER, which AC means active and ER means expert, to detect these servers. However, they are passive methods because they can only be established when the servers are in connection state. Current works on detecting Shadowsocks servers are mostly based on the features of servers’ data stream combined with machine learning. Hence, the study of identifying these servers is pretty crucial. Also, hackers can make use of these servers to threaten public network security, such as DDoS and Phishing attacks. It is quite difficult to identify these servers, which provides potential criminals with opportunities to commit crime. Anonymous server is created for hiding the information of hosts when they are surfing the Internet, such as Tor, Shadowsocks, etc.
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