Web Fingerprint Defense with Timing-assistant Traffic Burst Shaping

Website Fingerprinting (WF) is a traffic analysis attack that can identify the encrypted network activities of clients, allowing for the determination of which web pages the clients are accessing. Recently, many WF defense approaches have been proposed, such as WTF-PAD and Walkie-Talkie, which have now become Tors primary deployed defense mechanisms. However, these WF defense approaches have proven less effective when faced with deep learning technology. The identification accuracy of DF-based attack approaches exceeds by over 90%. To address the above problem, we present time-assisted traffic-shaping defense approach. Our approach integrates multi-website traffic based on timestamp information to incorporate all burst-level features. To create more confusion in website features, we use random time thresholds to segment bursts in different combinations for better shaping. We perform performance evaluations on DApps and find that our approach can significantly decrease the accuracy of DApps fingerprint attacks to less than 50%. Moreover, our approach is also effective in Tor scenarios.