Spoofing attack detection in dynamic channels with imperfect CSI

TL;DR

This paper introduces an adaptive Kalman filter-based method for detecting spoofing attacks in wireless channels, effectively handling imperfect CSI and dynamic environments to improve detection robustness.

Contribution

It proposes a novel Kalman residual-based detection scheme that compensates for channel estimation errors and adapts to time-varying scenarios, enhancing spoofing detection performance.

Findings

Improved detection accuracy at low SNR.

Enhanced robustness in dynamic channel conditions.

Effective mitigation of channel estimation errors.

Abstract

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.