Towards Robust Multi-tab Website Fingerprinting

TL;DR

This paper introduces ARES, a Transformer-based framework for multi-tab website fingerprinting that effectively identifies websites in complex browsing sessions, outperforming existing methods and resisting defenses.

Contribution

ARES is the first multi-tab WF attack framework using multi-label classification and Transformer models, addressing limitations of prior single-tab approaches.

Findings

ARES achieves high accuracy in multi-tab scenarios.

ARES remains robust against various WF defenses.

Extensive evaluation over large datasets confirms effectiveness.

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.