Attack Smarter: Attention-Driven Fine-Grained Webpage Fingerprinting Attacks

TL;DR

This paper introduces ADWPF, an attention-driven fine-grained webpage fingerprinting attack that improves website identification accuracy in complex, large-scale, multi-tab browsing scenarios by leveraging attention mechanisms and targeted data augmentation.

Contribution

The paper presents a novel attention-based approach for webpage fingerprinting that enhances classification in multi-tab and subpage scenarios, outperforming existing methods.

Findings

ADWPF achieves higher accuracy than state-of-the-art baselines.

Attention mechanisms effectively capture global contextual patterns.

Targeted augmentation improves model robustness.

Abstract

Website Fingerprinting (WF) attacks aim to infer which websites a user is visiting by analyzing traffic patterns, thereby compromising user anonymity. Although this technique has been demonstrated to be effective in controlled experimental environments, it remains largely limited to small-scale scenarios, typically restricted to recognizing website homepages. In practical settings, however, users frequently access multiple subpages in rapid succession, often before previous content fully loads. WebPage Fingerprinting (WPF) generalizes the WF framework to large-scale environments by modeling subpages of the same site as distinct classes. These pages often share similar page elements, resulting in lower inter-class variance in traffic features. Furthermore, we consider multi-tab browsing scenarios, in which a single trace encompasses multiple categories of webpages. This leads to overlapping traffic segments, and similar features may appear in different positions within the traffic, thereby increasing the difficulty of classification. To address these challenges, we propose an attention-driven fine-grained WPF attack, named ADWPF. Specifically, during the training phase, we apply targeted augmentation to salient regions of the traffic based on attention maps, including attention cropping and attention masking. ADWPF then extracts low-dimensional features from both the original and augmented traffic and applies self-attention modules to capture the global contextual patterns of the trace. Finally, to handle the multi-tab scenario, we employ the residual attention to generate class-specific representations of webpages occurring at different temporal positions. Extensive experiments demonstrate that the proposed method consistently surpasses state-of-the-art baselines across datasets of different scales.