Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication
This work addresses security for wireless communication systems, but it is incremental as it builds on existing protocols and compares known methods in a specific scenario.
The paper tackles the problem of physical layer authentication in time-varying fading channels with limited channel state information, comparing statistical decision methods and machine learning techniques; it finds that one-class classification algorithms achieve the lowest probability of missed detection under low spatial correlation, while statistical methods are better under high correlation.
In this paper we assess the security performance of key-less physical layer authentication schemes in the case of time-varying fading channels, considering both partial and no channel state information (CSI) on the receiver's side. We first present a generalization of a well-known protocol previously proposed for flat fading channels and we study different statistical decision methods and the corresponding optimal attack strategies in order to improve the authentication performance in the considered scenario. We then consider the application of machine learning techniques in the same setting, exploiting different one-class nearest neighbor (OCNN) classification algorithms. We observe that, under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a low spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is higher.