Mitigating Proxy-Induced Traffic Drift in Website Fingerprinting via Model-Agnostic Traffic Tailoring

2026-08-20

AI Summary

本文针对代理协议导致的网站指纹识别性能下降问题，提出PA3框架，通过分析和调整流量特征，提高模型在未见过的协议上的泛化能力。

Abstract

Website fingerprinting (WF) based on deep learning can effectively identify websites from encrypted traffic. However, users often rely on proxy protocols to bypass censorship, and the diversity of these protocols poses a major challenge, as WF models trained on traffic from one set of protocols perform poorly when evaluated on that from unseen protocols. We attribute this issue to proxy-induced feature drift, where traffic patterns of the same website vary with the proxy protocol, leading to discrepancies that WF models fail to capture and severe performance degradation. To tackle this issue, we propose PA3, a model-agnostic preprocessing framework to analyze and mitigate the proxy-induced drift. PA3 first fingerprints the protocol-specific drift. These fingerprints are then used to tailor the proxied traffic for feature alignment, which mitigates the drift and considerably improves the generalization of WF models on traffic from unseen protocols. Extensive evaluations demonstrate that PA3 substantially enhances generalization on unseen protocols with an average improvement of 0.12 in F1-score (roughly 27% relative), achieving up to a 0.41 absolute gain across models, which narrows the performance gap introduced by the drift. In the best case, PA3 enables WF models to obtain F1-scores above 0.96 on traffic from unseen protocols.

Problem

• Website Fingerprinting
• Proxy Protocols
• Feature Drift
• Traffic Patterns
• Model Generalization

Innovation

• model-agnostic
• proxy-induced drift
• traffic tailoring
• feature alignment