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

Yi Xie, Jiahao Feng, Wenju Huang, Yixi Zhang, Xueliang Sun, Xiaochou Chen, Xiapu Luo

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

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. Morevoer, 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 named Contrastive Fingerprinting (CF), which leverages contrastive learning and data augmentation over a few training traces. The results of extensive experiments on challenging datasets over few-shot traces demonstrate the high accuracy of the CF attack and its robustness against WF defenses. 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. The experimental results also show that CF outperforms two existing WF attacks with few-shot traces under different network conditions in real-world applications.
Original languageEnglish
Title of host publicationProceedings of the ACM on Web Conference 2024
PublisherAssociation for Computing Machinery
Pages1203–1214
Publication statusPublished - May 2024

Fingerprint

Dive into the research topics of 'Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot Traces'. Together they form a unique fingerprint.

Cite this