Cross-Environmental Website Fingerprinting

Despite the widespread adoption of encryption, such as TLS, encrypted proxies, and Tor, website fingerprinting (WF) has long been proven to be able to recognize web sites from encrypted traffic. However, existing WF methods were generally developed and evaluated under the implicit assumption that traffic samples for training and recognition are captured in the same environment. When applied to diverse environments affected by practical factors, such as various browsers and proxy software, they will be hampered by three-fold challenges: i) feature drift, ii) sampling dilemma, and iii) few-shot generalization. None of existing WF methods can fully address them. In this paper, we take the first step to cross-environmental WF and advance a systematic framework, dubbed X-EPRINT, to tackle the above challenges. X - EPRINT generates cross-environmentally invariant features to address feature drift. It mitigates sampling dilemma via potential-aware traffic resampling. X-EPRINT capitalizes on inter-flow data augmentation to solve few-shot generalization. We conduct extensive experiments to evaluate X-EPRINT. The experimental results demonstrate that X - EPRINT achieves a robust performance in zero-shot cross-environmental recognition, with an F1-score of 0.719, which is 58.4% higher than the top-performing baseline method. It also attains an F1-score of 0.925 in 3-shot recognition, fulfilling few-shot environment adaptation.