信息安全研究2025，Vol.11Issue(5)：447-456,10.DOI:10.12379/j.issn.2096-1057.2025.05.07
基于改进双向记忆残差网络的Tor流量分类研究
Research on Tor Traffic Classification Based on Improved Bidirectional Memory Residual Network
摘要
Abstract
In order to solve the problem of difficulty in correctly classifying Tor traffic and regulating it due to the encryption characteristics of Tor links,a Tor traffic classification method based on an improved bidirectional memory residual neural network(CBAM-BiMRNet)is proposed.Firstly,the SMOTETomek(SMOTE and Tomek links)comprehensive sampling algorithm is adopted to balance the dataset,so that the model could learn from the traffic data of all categories.Secondly,CBAM is used to assign greater weights to important features,combining 1D convolution with bidirectional long short-term memory modules to extract temporal and local spatial features of Tor traffic data.Finally,by adding identity maps,the phenomenon of gradient vanishing and exploding caused by the increase in model layers was avoided,and the problem of network degradation was solved.The experimental results show that on the ISCXTor2016 dataset,the accuracy of our model for Tor traffic recognition reached 99.22％,and the accuracy for Tor traffic application service type classification reached 93.10％,proving that the model can effectively recognize and classify Tor traffic.关键词
Tor流量/残差网络/流量识别/综合采样/类别不平衡Key words
Tor traffic/residual network/traffic identification/integrated sampling/class imbalance分类
计算机与自动化引用本文复制引用
唐妍,王恒,马自强,滕海龙,施若涵,张宁宁..基于改进双向记忆残差网络的Tor流量分类研究[J].信息安全研究,2025,11(5):447-456,10.基金项目
宁夏回族自治区重点研发计划一般项目(2022BDE03008) （2022BDE03008）
宁夏回族自治区重点研发计划引才专项(2021BEB04004,2021BEB04047) （2021BEB04004,2021BEB04047）
