FD-WF: A Multi-tab Website Fingerprinting Attack Based on Fixed Dimensions for Tor Network

Website Fingerprinting Attack (WFA) is an effective method of network monitoring, which analyzes network traffic to identify the specific website or web page that a user is browsing. The performance of previous WFA, which assume singletab scenarios, deteriorates significantly in real-world multi-tab environments. While this issue has been acknowledged and some research has been conducted on multi-tab WFA, limitations in the accuracy and efficiency of multi-tab classification still persist. Specifically, extended research on mixed-tab scenarios, where the number of tabs is unknown, still lacks sufficient attention. In this paper, we propose FD-WF, a multi-tab WFA model based on fixed dimensions on the Tor network. This model mitigates the issue of blurred features in single-tab images caused by the expansion of multi-tab mixed traffic sequences. It enhances the ability to accurately identify and classify multiple web pages users are browsing. We propose a minimum padding optimization function to improve the performance of fixed-dimension image generation and introduce an enhanced ResNet-18 model to better address the fingerprint classification challenge for multi-tab scenarios. The experimental results verified the feasibility and effectiveness of the proposed method. While ensuring the performance of singletab WFA, our model achieved a F1 Score of 96% for multi-tab web pages. Even in the more complex mixed-tab scenario, we still achieved an F1 Score of 78%.