Flow Sequence-Based Anonymity Network Traffic Identification with Residual Graph Convolutional Networks

Identifying anonymity services from network traffic is a crucial task for network management and security. Currently, some works based on deep learning have achieved excellent performance for traffic analysis, especially those based on flow sequence (FS), which utilizes information and features of the traffic flow. However, these models still face a serious challenge because of lacking a mechanism to take into account relationships between flows, resulting in mistakenly recognizing irrelevant flows in FS as clues for identifying traffic. In this paper, we propose a novel FS-based anonymity network traffic identification framework to tackle this problem, which leverages Residual Graph Convolutional Network (ResGCN) to exploit relationships between flows for FS feature extraction. Moreover, we design a practical scheme to preprocess the raw data of real-world traffic, which further improves identification performance and efficiency. Experimental results on two real-world traffic datasets demonstrate that our method outperforms state-of-the-art methods by a large margin.