A Dynamic GAN-Based Obfuscation Approach Against Profiling Attacks

The rapid growth in the number of wireless IoT devices has introduced new privacy challenges. Adversaries can exploit captured wireless traffic to profile and identify specific devices within the network. While several device identification frameworks have been developed to classify and identify devices, researchers have proposed methods such as traffic padding, shaping, and cover traffic injection to counteract identification attempts. However, these approaches often prove ineffective when adversaries gain access to the network. In this study, we propose a GAN-based traffic generation and injection framework designed to enhance device privacy against traffic analysis attacks, even in the presence of local adversaries. Our approach generates highly realistic network traffic and achieves a 98.5% success rate in seamlessly injecting packets into the network without any observable issues. By reducing the prediction confidence of multiple device identification approaches in predicting the origin of network traffic, our framework effectively enhances the privacy of IoT devices. Furthermore, the proposed solution incurs minimal performance overhead, making it a practical and efficient approach to addressing the growing privacy challenges in wireless networks.