Pilot Spoofing Attack Detection and Localization With Mobile Eavesdropper

Due to the public nature of wireless environments, legitimate communication systems are generally under the threat of Pilot Spoofing Attack (PSA). The malicious user may manipulate channel estimation by emitting the same pilot information, leading to the biased estimation of Channel State Information (CSI) and the degraded secrecy capacity. Although various methods have been proposed for combating PSA, the ubiquitous mobility of legitimate and malicious users introduces complex time-varying characteristics, making previous static PSA detection methods less attractive. In this paper, we present a novel location-awareness dynamical PSA detection mechanism. To model the complex dynamical behaviors, a Random Finite Set (RFS) is formulated to jointly describe the mobile positions and uncertain attack status. On this basis, we design a joint PSA detection and user localization algorithm relying on the sequential Bayesian inference. As such, the inherent correlations in mobile patterns can be exploited to effectively enhance PSA detection probability and localization accuracy. Numerical simulation results validate that the new method significantly improves PSA detection and CSI estimation accuracy compared with state-of-the-art counterparts, therefore the information leakage problem is greatly alleviated. Our new approach thus has the great potential in the emerging mobile scenarios by effectively enhancing the physical-layer secured transmissions.