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ABSTRACT Tor is a popular low-latency anonymous communication system that protects user privacy via layered encryption. However, website fingerprinting (WF) attacks can still infer browsing behavior by analyzing statistical patterns in encrypted traffic. Existing multi-tab WF methods primarily rely on temporal features and employ fixed attention windows that treat all traffic segments uniformly, ignoring the fact that multi-tab traffic exhibits varying degrees of overlap across different time periods. To address these limitations, we propose CRAFT, a Complexity-Routed Attention framework with Frequency-Temporal fusion for multi-tab website fingerprinting. First, we introduce a dual-branch feature extractor that combines a temporal convolutional backbone with a frequency-domain branch for capturing periodic traffic rhythms, fused via a learned gating mechanism. Second, we design a complexity-aware adaptive routed attention mechanism, where a lightweight router predicts per-token traffic mixing complexity and dynamically adjusts the attention scope through a Gaussian distance bias, focusing locally on simple single-site segments while expanding to broader context in complex overlapping regions. Extensive experiments across multiple multi-tab settings demonstrate that CRAFT achieves strong and consistent improvements over existing approaches.
Pan et al. (Fri,) studied this question.