WFCAT: Augmenting Website Fingerprinting with Channel-wise Attention on Timing Features

Website Fingerprinting (WF) aims to deanonymize users on the Tor network by analyzing encrypted network traffic. Recent deep-learning-based attacks show high accuracy on undefended traces. However, they struggle against modern defenses that use tactics like injecting dummy packets and delaying real packets, which significantly degrade classification performance. Our analysis reveals that current attacks inadequately leverage the timing information inherent in traffic traces, which persists as a source of leakage even under robust defenses. Addressing this shortfall, we introduce a novel feature representation named the Inter-Arrival Time (IAT) histogram, which quantifies the frequencies of packet inter-arrival times across predetermined time slots. Complementing this feature, we propose a new CNN-based attack, WFCAT, enhanced with two architectural blocks designed to effectively extract and utilize timing information. The model employs convolutional kernels of varying sizes to capture multi-scale temporal features, which are then integrated through a weighted combination across feature channels. This channel-wise attention mechanism enables the model to adaptively emphasize informative patterns while suppressing noise, thereby improving its robustness against timing obfuscation. Our experiments validate that WFCAT substantially outperforms existing methods on defended traces in both closed- and open-world scenarios. Notably, WFCAT achieves over 59% accuracy against Surakav, a recently developed robust defense, marking an improvement of over 28% and 48% against the state-of-the-art attacks RF and Tik-Tok, respectively, in the closed-world scenario.