FingerMamba: Mamba-based Efficient Multi-tab Website Fingerprinting
Liu Lin, Ziling Wei, Shuhui Chen, Zixuan Dong, Jinshu Su · 2024
Nowadays, protecting user privacy on the Internet is paramount, especially with the increasing use of the Tor network to anonymize online activities. However, Tor is vulnerable to website fingerprinting (WF), where patterns in encrypted traffic are analyzed to infer visited websites. It can be utilized to monitor and investigate illegal activities on the dark web. Existing website fingerprinting techniques typically assume single-tab browsing, which is unrealistic as users often open multiple tabs consecutively or within a short period due to Tor’s slow loading speeds and typical user habits. Moreover, current multi-tab approaches face challenges in classification speed, which is crucial for high-throughput networks. FingerMamba, our proposed model, addresses these gaps by efficiently extracting local information and establishing long-range dependencies using a Mamba-based structured state-space model. It significantly enhances the accuracy and speed of multi-tab website fingerprinting. Extensive experiments on the largest real-world multi-tab dataset demonstrate that FingerMamba effectively improves classification accuracy in both closed-world and open-world settings. Furthermore, with maintaining similar accuracy performance, FingerMamba can increase inference speed by up to four times compared to the existing methods. To our knowledge, FingerMamba is the first model to tailor the Mamba architecture for website fingerprinting.