Robust Multi-tab Website Fingerprinting Attacks in the Wild

• Published in: 2023 IEEE Symposium on Security and Privacy (SP) pp. 1005 - 1022
• Main Authors: Deng, Xinhao, Yin, Qilei, Liu, Zhuotao, Zhao, Xiyuan, Li, Qi, Xu, Mingwei, Xu, Ke, Wu, Jianping
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2023
• Subjects: Cryptography; Fingerprint recognition; Privacy; Prototypes; Telecommunication traffic; Transformers
• ISSN: 2375-1207
• DOI: 10.1109/SP46215.2023.10179464

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.