通信学报2025，Vol.46Issue(6)：45-59,15.DOI:10.11959/j.issn.1000-436x.2025095
基于并行流量图和图神经网络的加密流量分类方法
Encrypted traffic classification method based on parallel traffic graph and graph neural network
摘要
Abstract
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 con-structed from the packet header and payload perspectives to emphasize their differences.Then,an improved graph atten-tion 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,clas-sification 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.关键词
加密流量分类/深度学习/图神经网络/特征融合Key words
encrypted traffic classification/deep learning/graph neural network/feature fusion分类
信息技术与安全科学引用本文复制引用
刘涛涛,付钰,俞艺涵,安义帅..基于并行流量图和图神经网络的加密流量分类方法[J].通信学报,2025,46(6):45-59,15.基金项目
国家自然科学基金资助项目(No.62102422) （No.62102422）
河南省科技攻关基金资助项目(No.242102211070) The National Natural Science Foundation of China(No.62102422),Henan Province Key Science and Techno-logy Research Project of China(No.242102211070) （No.242102211070）
