Study of the physical layer authentication

Statistical and Machine Learning-Based Decision Techniques for Physical Layer Authentication

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.

L. Senigagliesi, M. Baldi and E. Gambi, "Statistical and Machine Learning-Based Decision Techniques for Physical Layer Authentication," 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6.

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Blind Physical Layer Authentication over Fading Wireless Channels through Machine Learning

The problem of determining the source of a message in a wireless communication link is challenging, especially for those systems in which cryptographic approaches are barely feasible due to limited resources. In this paper we consider a physical layer authentication protocol based on the characteristics of the communication channel and exploiting machine learning techniques to obtain authentication without needing any statistical knowledge of the channel from the authenticator. Different operational conditions are taken into account, considering a set of parallel channels affected by time-varying fading and assuming correlation between an opponent’s channel and the authenticator’s channel. Nearest Neighbor (NN) classification is used for authentication, and since the authenticator has no access to forged messages during the training phase, one-class NN classification algorithms are considered. We show that a good secrecy performance with a small training set may be achieved, allowing detection of an attacker with a very high probability in most of the cases. On the other hand, aiming at guaranteeing security even in the case of rapidly varying channels, these techniques prove to be quite conservative, and exhibit a high probability of refusing uncertain messages even when they come from the legitimate transmitter.

L. Senigagliesi, L. Cintioni, M. Baldi and E. Gambi, "Blind Physical Layer Authentication over Fading Wireless Channels through Machine Learning," 2019 IEEE International Workshop on Information Forensics and Security (WIFS), Delft, Netherlands, 2019, pp. 1-6.

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