Website Fingerprinting with Packet Sampling: A More Realistic Approach in Real-World Networks

Website fingerprinting (WF) attack enables an eavesdropper to spy on users' browsing activity for malicious purposes, which poses a critical threat to Internet users' privacy. Prior research mainly focuses on attack performance in local area networks. Meanwhile, with the upgrade of network infrastruc-ture, a notable transition to high-speed connectivity in real-world network nodes has emerged. While in high-speed networks, there has been a mismatch between the overall traffic in transmission and the upper limit of the attacker's processing capabilities. Prior attacks based on full-traffic collection will face a sharp increase in resource overhead and a decrease in attack efficiency. In response to the emerging challenges, we first apply sampling techniques to WF attack to reduce the amount of data that needs to be processed. In addition, we devise an effective attack model with good performance on sampled traffic. Evaluations indicate that our attack model achieves 94.9 % accuracy in 1/8 packet sampling scenario and 98% accuracy in non-sampling scenario, outperforming the state-of-the-art in both cases. The compatibility in packet sampling environments helps extend the WF attack from the laboratory setting targeting at a few users to real-world high-speed networks capable of massive surveillance. The code of this paper is publicly available at https://github.com/code-flyerISAPWF.