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

2026-03-22

AI Summary

This work addresses the challenge of insufficient identification accuracy in existing website fingerprinting attacks under multi-label concurrent browsing scenarios. The authors propose a novel attack method based on a multi-granularity chunked Transformer, which employs multi-scale convolutional kernels to extract robust raw traffic features and incorporates a three-level hierarchical interaction mechanism that emulates the cognitive logic of “global reconnaissance followed by local scrutiny” to effectively model multiple target websites within mixed traffic. A router token–guided cross-granularity feature fusion strategy is introduced to enhance representation learning. The proposed approach consistently outperforms current state-of-the-art baselines across multiple datasets and under mainstream defensive mechanisms, achieving the best reported performance to date.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• website fingerprinting
• multi-tab browsing
• mixed traffic
• anonymous communication
• traffic analysis

Innovation

• multi-granularity
• patch-based Transformer
• website fingerprinting
• mixed traffic
• hierarchical interaction