A website fingerprinting attack with unsupervised out-of-distribution detection

Abstract Website fingerprinting attacks are critical for extracting website information and identifying illegal websites visited by users in anonymous networks such as Tor. However, existing attacks struggle to extract effective features from unmonitored websites due to the diversity. Although increasing unmonitored training data can improve effectiveness, it also increases attacker costs. To address this, we propose a novel website fingerprinting attack that leverages unsupervised Out-of-Distri