TMGAN: A GAN-Based Traffic Morphing Defense Against Website Fingerprinting

With the rapid growth of encrypted traffic, methods that use side-channel information to monitor online user behavior have emerged, known as Website Fingerprinting (WF) attacks. These attacks pose a significant threat to the privacy of users’ online activities. To address the threat posed by various WF attacks to network behavior privacy, current methods lack defenses based on the source/target misclassification of traffic. Our goal is to design a WF defense method that transforms the side-channel information of the given original category traffic into that of another category to counter existing DNN-based WF attacks. We utilize adversarial examples and employ a Generative Adversarial Network (GAN) incorporating a WF model to generate perturbations. These perturbations are overlaid onto the given traffic, morphing it into traffic of another category, thereby enhancing the privacy of network behavior. We refer to the proposed method as the Traffic Morphing Generative Adversarial Network (TMGAN). Experimental results demonstrate that this method effectively counters most WF attacks, enhancing the privacy of users’ online behavior. In white-box scenarios, it achieves average perturbation rates of 90-99% and morphing rates of 31-75%. In black-box scenarios, it achieves average perturbation rates of 95-98% and morphing rates of 5-28%.