Abstract
Virtual Private Network (VPN) technology is now widely used in various scenarios such as telecommuting. The importance of VPN traffic identification for network security and management has increased significantly with the development of proxy technology. Unlike other tasks such as application classification, VPN traffic has only one flow problem. In addition, the development of encryption technology brings new challenges to VPN traffic identification.
This paper proposes VT-GAT, a VPN traffic graph classification model based on Graph Attention Networks (GAT), to solve the above problems. Compared with existing VPN encrypted traffic classification techniques, VT-GAT solves the problem that previous techniques ignore the graph connectivity information contained in traffic. VT-GAT first constructs traffic behavior graphs by characterizing raw traffic data at packet and flow levels. Then it combines graph neural networks and attention mechanisms to extract behavioral features in the traffic graph data automatically. Extensive experimental results on the Datacon21 dataset show that VT-GAT can achieve over 99\(\%\) in all classification metrics. Compared to existing machine learning and deep learning methods, VT-GAT improves F1-Score by about 3.02%–63.55%. In addition, VT-GAT maintains good robustness when the number of classification categories varies. These results demonstrate the usefulness of VT-GAT in the VPN traffic classification.
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Notes
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The dataset can be found at https://anonymous.4open.science/r/VPN_Traffic_Graph_Dataset-EDA0. Researchers who use the dataset should indicate the source of data by citing this paper.
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Acknowledgements
We thank the anonymous reviewers for their insightful comments. This work was supported in part by the National Key Research and Development Program of China under Grant No. 2019YFB1005205.
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Xu, H., Li, S., Cheng, Z., Qin, R., Xie, J., Sun, P. (2022). VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network. In: Gao, H., Wang, X., Wei, W., Dagiuklas, T. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 461. Springer, Cham. https://doi.org/10.1007/978-3-031-24386-8_24
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