Spoofing Detection with Reinforcement Learning in Wireless Networks

In this paper, we investigate the PHY-layer authentication in wireless networks, which exploits PHY-layer channel information such as the received signal strength indicators to detect spoofing attacks. The interactions between a legitimate receiver node and a spoofer are formulated as a PHY- authentication game. More specifically, the receiver chooses the test threshold in the hypothesis test of the spoofing detection to maximize its expected utility based on Bayesian risk to detect the spoofer. On the other hand, the spoofing node decides its attack strength, i.e., the frequency to send a spoofing packet that claims to use another node's MAC address, based on its individual utility in the zero-sum game. As it is challenging for most radio nodes to obtain the exact channel models in advance in a dynamic radio environment, we propose a spoofing detection scheme based on reinforcement learning techniques, which achieves the optimal test threshold in the spoofing detection via Q-learning and implement it over universal software radio peripherals (USRP). Experimental results are presented to validate its efficiency in spoofing detection.