Towards Robust Multi-tab Website Fingerprinting
This addresses a critical limitation in website fingerprinting for eavesdroppers, enabling more effective attacks on user privacy in multi-tab browsing, though it is incremental as it builds on prior WF methods.
The paper tackles the problem of accurately identifying websites in multi-tab browsing sessions over encrypted connections, where existing website fingerprinting attacks fail due to disrupted traffic patterns, and proposes ARES, a novel framework that achieves optimal performance in realistic scenarios and remains robust against defenses.
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.