KimeraPAD: A Novel Low-Overhead Real-Time Defense Against Website Fingerprinting Attacks Based on Deep Reinforcement Learning

The onion router (Tor) is a network system for anonymous communication. However, website fingerprinting (WF) attacks have threatened the anonymity of Tor. WF attackers can passively monitor and collect traffic, classify the victims' traffic based on machine learning or deep learning, and identify the websites the victims visit. In recent years, there has been some research on WF defense, but most of the works have high bandwidth and latency overhead. Besides, some defenses are criticized as being unrealistic to implement in real-time due to the need for prior knowledge of the traffic's exact packet sequences, and the lengths of sequences. In this paper, we propose KimeraPAD, a defense against WF attacks based on deep reinforcement learning. Specifically, our method first trains an agent to generate perturbations confusing the attacker's classifier. To overcome the weak point of WF defense based on adversarial learning, we then design the implementation method and incur randomness so that it can inject dummy packets in real time and resist adversarial training. Experimental results demonstrate that our method can greatly reduce attack accuracy with a low bandwidth overhead. Besides, KimeraPAD can also be implemented on the client side, which simplifies the implementation a lot while achieving excellent performance.