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arXiv · 2501.12622

Towards Robust Multi-tab Website Fingerprinting

Xinhao Deng·Xiyuan Zhao·Qilei Yin·Zhuotao Liu·Qi Li·Mingwei Xu·Ke Xu·Jianping Wu
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Abstract

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using the novel Transformer-based models. Specifically, ARES extracts local patterns based on multi-level traffic aggregation features and utilizes the improved self-attention mechanism to analyze the correlations between these local patterns, effectively identifying websites. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale datasets collected over multiple months. The experimental results illustrate that ARES achieves optimal performance in several realistic scenarios. Further, ARES remains robust even against various WF defenses.

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Xinhao Deng, Xiyuan Zhao, Qilei Yin, Zhuotao Liu, Qi Li, Mingwei Xu, Ke Xu, Jianping Wu. 2025-01-22. Towards Robust Multi-tab Website Fingerprinting. https://arxiv.org/abs/2501.12622

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