Robust Multi-tab Website Fingerprinting Attacks in the Wild

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 a multi-classifier framework. Each classifier, designed based on a novel transformer model, identifies a specific website using its local patterns extracted from multiple traffic segments. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale dataset collected over multiple months (by far the largest multi-tab WF dataset studied in academic papers.) The experimental results illustrate that ARES effectively achieves the multi-tab WF attack with the best F1-score of 0.907. Further, ARES remains robust even against various WF defenses.