Encrypted traffic classification method based on parallel traffic graph and graph neural network

被引：0

作者：

Liu, Taotao ^{[1
]}

Fu, Yu ^{[1
]}

Yu, Yihan ^{[2
]}

An, Yishuai ^{[1
]}

机构：

[1] Department of Information Security, Naval University of Engineering, Wuhan

[2] Department of Operational Operations and Planning, Naval University of Engineering, Wuhan

来源：

Tongxin Xuebao/Journal on Communications | 2025年 / 46卷 / 06期

基金：

中国国家自然科学基金;

关键词：

deep learning; encrypted traffic classification; feature fusion; graph neural network;

D O I：

10.11959/j.issn.1000-436x.2025095

中图分类号：

学科分类号：

摘要：

Aiming at the problems of traditional encrypted traffic classification methods limited by the imbalance of data-set classes and the unreliability of the features used in complex network environments, an encrypted traffic classification method based on parallel traffic graph and graph neural network was proposed. Firstly, the traffic graphs were constructed from the packet header and payload perspectives to emphasize their differences. Then, an improved graph attention network was introduced to extract effective information from the parallel traffic graphs. Next, a feature cross-fusion attention module was used to fuse the extracted information, achieving a more robust feature representation. Finally, classification was performed using fully connected layers and a Softmax layer. Experiments show that the proposed method achieves better results on the ISCX-VPN, ISCX-nonVPN, ISCX-Tor, and ISCX-nonTor datasets, with accuracies of 96.88%, 90.62%, 99.24%, and 98.13%, respectively, significantly enhancing encrypted traffic classification performance. © 2025 Editorial Board of Journal on Communications. All rights reserved.

引用

页码：45 / 59

页数：14

共 33 条

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