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