Machine Learning-Based PHY-Authentication Without Prior Attacker Information for Wireless Multiple Access Channels

Physical layer (PHY) authentication methods provide spatial security by exploiting the unique channel between two users. In recent years, many studies focused on substituting traditional threshold-based detection mechanisms with machine/deep learning classifiers to solve the threshold selection problem and obtain better detection accuracy. However, these studies assume that receivers have access to spoofer's channel information at the training of the classifier, which is unrealistic for real-time scenarios. In this study, we propose a PHY-authentication architecture for wireless multiple access channels (W-MACs) that removes this assumption and works without any prior information about the spoofer. The proposed method is designed for multi-user systems and is suitable for any classifier model or communication protocol. The feasibility and the performance of the proposed method are investigated via computer simulations and compared with a benchmark model. The results proved the feasibility of the proposed method as it can detect spoofers successfully without requiring spoofers' channel information.