@article{Deng2026, 
author = {Xinhao Deng and Jingyou Chen and Linxiao Yu and Yixiang Zhang and Zhongyi Gu and Changhao Qiu and Xiyuan Zhao and Ke Xu and Qi Li},
title = {Beyond a Single Perspective: Towards a Realistic Evaluation of Website Fingerprinting Attacks},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1381-1392},
keywords = {deep learning, traffic analysis, Website Fingerprinting (WF)},
url = {https://www.sciopen.com/article/10.26599/TST.2025.9010167},
doi = {10.26599/TST.2025.9010167},
abstract = {Website Fingerprinting (WF) attacks exploit patterns in encrypted traffic to infer the websites visited by users, posing a serious threat to anonymous communication systems. Although recent WF techniques achieve over 0.9 accuracy in controlled experimental settings, most studies remain confined to single scenarios, overlooking the complexity of real-world environments. This paper presents the first systematic and comprehensive evaluation of existing WF attacks under diverse realistic conditions, including defense mechanisms, traffic drift, multi-tab browsing, early-stage detection, open-world settings, and few-shot scenarios. Experimental results show that many WF techniques with strong performance in isolated settings degrade significantly when facing other conditions. Since real-world environments often combine multiple challenges, current WF attacks are difficult to apply directly in practice. This study highlights the limitations of WF attacks and introduces a multidimensional evaluation framework, offering critical insights for developing more robust and practical WF attacks.}
}