TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning

Website fingerprinting (WF) attacks are methods that monitor user traffic patterns to identify which websites they visit, threatening privacy even on protected networks. Existing defenses slow down these attacks but can be defeated when attackers retrain their models, and they also add significant slowness to network traffic. TrapFlow, a new defense technique, uses backdoor learning (injecting hidden trigger patterns into website traffic) to trick attackers' AI models into making wrong predictions, either by memorizing false patterns during training or by being confused at inference time (when making predictions on new data).