  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.
