From Traces to Packets: Realistic Deep Learning Based Multi-Tab Website Fingerprinting Attacks

Abstract

Recent advancements in deep learning (DL) have introduced new security challenges in the form of side-channel attacks. A prime example is the website fingerprinting attack (WFA), which targets anonymity networks like Tor, enabling attackers to unveil users' protected browsing activities from traffic data. While state-of-the-art WFAs have achieved remarkable results, they often rely on unrealistic single-website assumptions. In this paper, we undertake an exhaustive exploration of multi-tab website fingerprinting attacks (MTWFAs) in more realistic scenarios. We delve into MTWFAs and introduce MTWFA-SEG, a task involving the fine-grained packet-level classification within multi-tab Tor traffic. By employing deep learning models, we reveal their potential to threaten user privacy by discerning visited websites and browsing session timing. We design an improved fully convolutional model for MTWFA-SEG, which are enhanced by both network architecture advances and traffic data instincts. In the evaluations on interlocking browsing datasets, the proposed models achieve remarkable accuracy rates of over 68.6%, 71.8%, and 76.1% in closed, imbalanced open, and balanced open-world settings, respectively. Furthermore, the proposed models exhibit substantial robustness across diverse train-test settings. We further validate our designs in a coarse-grained task, MTWFA-MultiLabel, where they not only achieve state-of-the-art performance but also demonstrate high robustness in challenging situations.