A Robust and Accurate Encrypted Video Traffic Identification Method via Graph Neural Network

作者：Li Zhao, Jiangchao Chen, Xiaoqing Ma, Meijie Du, Zhongyi Zhang, Qingyun Liu · 年份：2023 · DOI：10.1109/cscwd57460.2023.10152581 · 被引用次数：6 · 研究领域：Internet Traffic Analysis and Secure E-voting、Advanced Steganography and Watermarking Techniques、Network Security and Intrusion Detection

The explosive growth of video traffic has brought major challenges for network providers to improve user experience. On account of traffic encryption, network providers need to identify encrypted video traffic first before adopting optimization approaches to them. Traditional encrypted video traffic identification methods try to reveal the pattern of video traffic by using statistical features, which are not robust enough in different network environments. Some sophisticated graph-based methods recently have shown their advantages for encrypted traffic identification. However, these works lack optimization when it comes to the video streaming scenario. Inspired by these works, we propose GraphV, a GNN-based approach for identifying encrypted video traffic. Specifically, we construct an information-rich graph structure enhanced by unique features of video transmission. Then the embedding representation of each graph can be obtained through a Bi-LSTM layer added to all the sequential nodes embedding on this graph. The experiments on a well-known dataset and two open-world datasets from different network environments we collected show that GraphV outperforms the existing methods, especially on the generalization ability of the model.