Tor traffic identification based on federated learning using data temporal series
Qingqing Ren, Qingpeng Wang, Tao Guo, Qiang Huang, Mei Zhao · 2023
Tor, as an anonymous communication network tool, presents challenges in terms of identification due to its anonymous and multi-encrypted characteristics. The advent of deep learning has provided new solutions for identifying this type of traffic. However, in practical scenarios, sharing large amounts of traffic data is often not feasible. To address this issue, this paper proposes a Tor traffic identification scheme based on federated learning. By processing distributed data on federated learning clients, we can extract bidirectional temporal information from the traffic and perform model training. Utilizing federated learning, we can aggregate model parameters and achieve distributed identification and classification of Tor traffic. Experimental evaluation was conducted using a publicly available dataset to validate the effectiveness of our proposed approach.