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InfoResearchPeer-reviewed
TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning
- Published
- Record updated
Summary
TrapFlow is a website fingerprinting defense based on backdoor learning. It injects crafted trigger sequences into targeted website traffic so that an attacker's model learns incorrect associations, whether it retrains on the noisy data or trains only on clean data. In experiments, it cut the accuracy of the RF attack from 99% to 6% with 74% data overhead, and the authors also validated it in a real Tor network environment.