K–L Divergence-Based Detection of Attacks on Remote Control: The Utilization of Local Information
Fuyi Qu, Nachuan Yang, Hao Liu, Yuzhe Li · IEEE Transactions on Industrial Informatics · 2024
This article explores the security control in a remote control system driven by local and remote controllers. By utilizing the information of the local controller (namely, local information, including its mean and error variance), we propose a new actuator-side detector that can prevent performance degradation caused by attacks on the remote control signal, which is transmitted to the actuator through wireless communication. Besides, it can also overcome the difficulties when a standard Kullback–Leibler divergence detector fails to detect such attacks before the control signal is input into the system due to the unavailability of innovation$z_{k}$or measurement$y_{k}$. Subsequently, we characterize the corresponding impacts of different attack patterns on the estimation performance under the proposed detector. Based on this, we offer a compensation mechanism to improve the performance of the remote estimator under the attack. Finally, simulations are provided to illustrate the developed results.