Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot Traces

Yi Xie, Jiahao Feng, WenJu Huang, Yixi Zhang, Sunxueliang, XiaoChou Chen, Xiapu Luo

Keywords: Website fingerprinting, User privacy, Tor, Contrastive learning, Few-shot learning

Abstract: Website Fingerprinting (WF) attacks enable passive adversaries to identify the website a user visits over encrypted or anonymized network connections. WF attacks based on deep learning have achieved high accuracy in identifying websites based on abundant training traffic traces per website. However, collecting large-scale and fresh traces is quite cost-consuming and unrealistic. Moreover, these deep-learning-based WF attacks lack flexibility because they require a long bootstrap time for retraining when facing new traffic traces with different distributions or newly added monitored websites. This paper proposes a high-accuracy WF attack, Contrastive Fingerprinting (CF), which leverages contrastive learning and data augmentation over a few training traces. Extensive experiments have validated the accuracy and robustness of the CF attack on challenging datasets, which only collect a few training traces from each website and identify the testing traces with different distributions. For example, when each monitored website only has 20 training traces, CF identifies monitored websites with a high accuracy of 90.4% in the closed-world scenario and distinguishes monitored websites with a high True Positive Rate of 91.2% in the open-world scenario. We also show that CF outperforms two existing WF attacks with few-shot traces and has strong practicability.

Track: Responsible Web

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Submission Number: 1561