Research Article
VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network
@INPROCEEDINGS{10.1007/978-3-031-24386-8_24, author={Hongbo Xu and Shuhao Li and Zhenyu Cheng and Rui Qin and Jiang Xie and Peishuai Sun}, title={VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 18th EAI International Conference, CollaborateCom 2022, Hangzhou, China, October 15-16, 2022, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2023}, month={1}, keywords={Traffic classification VPN Encrypted traffic Graph attention networks Graph classification}, doi={10.1007/978-3-031-24386-8_24} }- Hongbo Xu
Shuhao Li
Zhenyu Cheng
Rui Qin
Jiang Xie
Peishuai Sun
Year: 2023
VT-GAT: A Novel VPN Encrypted Traffic Classification Model Based on Graph Attention Neural Network
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-24386-8_24
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.
