网络与信息安全学报2025，Vol.11Issue(6)：131-143,13.DOI:10.11959/j.issn.2096-109x.2025070
基于半监督学习的Tor匿名网络加密流量分类方法研究
Study of semi-supervised learning based encrypted traffic classification method for Tor anonymity network
摘要
Abstract
The onion routing(Tor)anonymous network,due to its multi-layer encryption and dynamic relay mecha-nisms,posed significant challenges for traffic classification.Illegal applications or websites traffic hidden within Tor anonymous network traffic could not be effectively detected by traditional traffic identification techniques.To address this issue a semi-supervised learning-based method for encrypted traffic classification in Tor networks was proposed.A unified"pretraining+fine-tuning"framework was utilized to fully leverage unlabeled data.In the pre-training phase,a dynamic masking mechanism was introduced to gradually adjust the masking ratio,enhancing the model's ability to learn latent features.In the fine-tuning phase,a channel and spatial attention(CSA)module was integrated to improve the model's sensitivity to critical local regions.The overall approach was built on a Transformer-based architecture,combining self-supervised feature extraction with enhanced discriminative capabil-ity.Experimental results on the ISCXTor2016 dataset and a self-collected dark web traffic dataset showed that the proposed method achieved over 98%across all performance metrics in classifying eight typical traffic categories,demonstrating excellent generalization ability and practical value.关键词
Tor匿名网络流量/半监督学习/暗网/TransformerKey words
Tor anonymous network traffic/semi-supervised learning/dark Web/Transformer分类
信息技术与安全科学引用本文复制引用
CAO Qingwei,GONG Liangyi,LI Yufu,MO Xiuliang,YANG Ke,YUAN Yali..基于半监督学习的Tor匿名网络加密流量分类方法研究[J].网络与信息安全学报,2025,11(6):131-143,13.基金项目
国家重点研发计划(2023YFB3106700) The National Key Research and Development Program of China(2023YFB3106700) （2023YFB3106700）
