TY - GEN
T1 - Contrastive Fingerprinting: A Novel Website Fingerprinting Attack over Few-shot Traces
AU - Xie, Yi
AU - Feng, Jiahao
AU - Huang, Wenju
AU - Zhang, Yixi
AU - Sun, Xueliang
AU - Chen, Xiaochou
AU - Luo, Xiapu
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
UR - https://doi.org/10.1145/3589334.3645575
M3 - Conference article published in proceeding or book
SP - 1203
EP - 1214
BT - Proceedings of the ACM on Web Conference 2024
PB - Association for Computing Machinery
ER -