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Recently, channel state information (CSI) at the physical-layer has beenutilized to detect spoofing attacks in wireless communications. However, due tohardware impairments and communication noise, the CSI cannot be estimatedaccurately, which significantly degrades the attack detection performance.Besides, the reliability of CSI based detection schemes is challenged bytime-varying scenarios. To address these issues, we propose an adaptive Kalmanbased detection scheme. By utilizing the knowledge of the predicted channel weeliminate the channel estimation error, especially the random phase error whichoccurs due to the lack of synchronization between transmitter and receiver.Furthermore, we define a Kalman residual based test statistic for attackdetection. Simulation results show that our proposed scheme makes the detectionmore robust at low signal-to-noise ratio (SNR) and in dynamic scenarios.

Spoofing attack detection in dynamic channels with imperfect CSI