Consistent Identification for FIR Systems With Multilevel Quantized Observations Subjected to Data Tampering Attacks: A Joint Estimation Approach

With the rapid development of artificial intelligence technology and Internet of Things technology, cyber–physical systems (CPSs) have also been fully developed, which promotes the widespread use of CPS in a variety of areas and brings many new opportunities and challenges. This article considers the consistent identification of finite impulse response (FIR) systems with multilevel quantized observations subjected to data tampering attacks. First, a defense algorithm based on known parameters is designed for the case where the attack is known. Second, on the basis of the algorithm with known attack, a joint consistent estimation approach is proposed for estimating system parameters, and the convergence and asymptotic normality of the algorithm are proved. Then, according to the asymptotic normality of the algorithm, the trace of the defense algorithm’s asymptotic error covariance matrix is used as the defense index to establish the optimal defense model. Finally, the effectiveness of the defense strategy is verified through experiments.