Class Incremental Website Fingerprinting Attack Based on Dynamic Expansion Architecture

Encrypted traffic on anonymizing networks is still at risk of being exposed to the Website Fingerprinting (WF) attack. This attack can seriously threaten the online privacy of users of anonymity networks such as Tor. While deep-learning-based WF attacks achieve high accuracy in controlled experimental settings, they cannot continuously learn after deployment. In real-world environments, new websites are constantly emerging, requiring attackers to expand their monitoring scope continuously. This necessitates attack models capable of continuous learning and expanding classification capabilities. In this paper, we explore how attackers can leverage incremental class learning techniques to continuously learn new classes while retaining the ability to distinguish old ones. This approach mitigates the catastrophic forgetting problem in dynamic, open-world scenarios. We introduce a new WF attack, Class Incremental Fingerprinting (CIF), which employs a scalable architecture enabling Class Incremental Learning (CIL) with limited resources. We evaluate this attack in various scenarios, such as learning 100, 200, and 500 monitored website classes across 5 and 10 incremental tasks, achieving an average accuracy of 97.8% and above. Additionally, we assess the CIF attack’s effectiveness in open-world multi-classification scenarios and test it in few-shot settings using the proposed data augmentation method, Mixtam, achieving an average task accuracy of 87.6% and above with only 30 samples per class.