PrismWF: A Multi-Granularity Patch-Based Transformer for Robust Website Fingerprinting Attack

TL;DR

PrismWF introduces a multi-granularity patch-based Transformer that significantly improves website fingerprinting attack accuracy in multi-tab browsing scenarios by effectively modeling mixed traffic patterns.

Contribution

The paper presents a novel multi-granularity patch-based Transformer architecture tailored for multi-tab website fingerprinting attacks, enhancing effectiveness over existing methods.

Findings

Achieves state-of-the-art accuracy on multiple datasets.

Effectively models mixed traffic in multi-tab scenarios.

Outperforms existing WF attack baselines.

Abstract

Tor is a low-latency anonymous communication network that protects user privacy by encrypting website traffic. However, recent website fingerprinting (WF) attacks have shown that encrypted traffic can still leak users' visited websites by exploiting statistical features such as packet size, direction, and inter-arrival time. Most existing WF attacks formulate the problem as a single-tab classification task, which significantly limits their effectiveness in realistic browsing scenarios where users access multiple websites concurrently, resulting in mixed traffic traces. To this end, we propose PrismWF, a multi-granularity patch-based Transformer for multi-tab WF attack. Specifically, we design a robust traffic feature representation for raw web traffic traces and extract multi-granularity features using convolutional kernels with different receptive fields. To effectively integrate information across temporal scales, the proposed model refines features through three hierarchical interaction mechanisms: inter-granularity detail supplementation from fine to coarse granularities, intra-granularity patch interaction with dedicated router tokens, and router-guided dual-level intra- and cross-granularity fusion. This design aligns with the cognitive logic of global coarse-grained reconnaissance and local fine-grained querying, enabling effective modeling of mixed traffic patterns in WF attack scenarios. Extensive experiments on various datasets and WF defenses demonstrate that our method achieves state-of-the-art performance compared to existing baselines.