We study the problem of identifying Finite Impulse Response (FIR) systems against random replay attacks with binary-valued observations in this paper. Replay attacks are modeled and the impact of attack strategies on the performance of parameter estimation algorithms is investigated. A defense algorithm that is consistent even during attacks is designed, and the problem of identifiability for the unknown parameters is discussed. Then, the asymptotic normality of this algorithm is given, and we derive the optimal defense strategy based on this. The proposed method’s rationality is verified by simulation example.

Publication:

AUTOMATICA

http://dx.doi.org/10.1016/j.automatica.2024.112001

Author:

Jin Guo

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, PR China

Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, PR China

guojin@ustb.edu.cn

Qingxiang Zhang

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, PR China

zmaster1001@163.com

Yanlong Zhao

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, PR China

Corresponding author.

ylzhao@amss.ac.cn