Hua Wu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration Southeast University, Ministry of Education, Jiangsu Nanjing, Purple Mountain Laboratories for Network and Communication Security (Nanjing, Jiangsu)), Shuyi Guo, Guang Cheng, Xiaoyan Hu (School of Cyber Science & Engineering and Key Laboratory of Computer Network and Information Integration Southeast University, Ministry of Education, Jiangsu Nanjing)
Due to the concealment of the dark web, many criminal activities choose to be conducted on it. The use of Tor bridges further obfuscates the traffic and enhances the concealment. Current researches on Tor bridge detection have used a small amount of complete traffic, which makes their methods not very practical in the backbone network. In this paper, we proposed a method for the detection of obfs4 bridge in backbone networks. To solve current limitations, we sample traffic to reduce the amount of data and put forward the Nested Count Bloom Filter structure to process the sampled network traffic. Besides, we extract features that can be used for bridge detection after traffic sampling. The experiment uses real backbone network traffic mixed with Tor traffic for verification. The experimental result shows that when Tor traffic accounts for only 0.15% and the sampling ratio is 64:1, the F1 score of the detection result is maintained at about 0.9.