WF-Transformer: Learning Temporal Features for Accurate Anonymous Traffic Identification by Using Transformer Networks

计算机科学 变压器 卷积神经网络 特征提取 实时计算 深度学习 交通分类 人工智能 计算机网络 电压 工程类 电气工程 服务质量
作者
Qiang Zhou,Liangmin Wang,Huijuan Zhu,Tong Lu,Victor S. Sheng
出处
期刊:IEEE Transactions on Information Forensics and Security [Institute of Electrical and Electronics Engineers]
卷期号:19: 30-43 被引量:4
标识
DOI:10.1109/tifs.2023.3318966
摘要

Website Fingerprinting (WF) is a network traffic mining technique for anonymous traffic identification, which enables a local adversary to identify the target website that an anonymous network user is browsing. WF attacks based on deep convolutional neural networks (CNN) get the state-of-the-art anonymous traffic classification performance. However, due to the locality restriction of CNN architecture for feature extraction on sequence data, these methods ignore the temporal feature extraction in the anonymous traffic analysis. In this paper, we present Website Fingerprinting Transformer (WF-Transformer), a novel anonymous network traffic analysis method that leverages Transformer networks for temporal feature extraction of traffic traces and improves the classification performance of Tor encrypted traffic. The architecture of WF-Transformer is specially designed for traffic trace processing and can classify anonymous traffic effectively. Furthermore, we evaluate the performance of WF-Transformer in both closed-world and open-world scenarios. In the closed-world scenario, WF-Transformer attains 99.1% accuracy on Tor traffic without defenses, better than state-or-the-art attacks, and archives 92.1% accuracy on the traces defended by WTF-PAD method. In the open-world scenario, WF-Transformer has better precision and recall on both defended and non-defended traces. Furthermore, WF-Transformer with a short input length (2000 cells) outperforms the DF method with a long input length (5000 cells).
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