Attack detection and fault-tolerant control of interconnected cyber-physical systems against simultaneous replayed time-delay and false-data injection attacks

Nowadays, interconnected cyber-physical systems (CPSs) are widely used with increasing deployments of industrial internet of things (IIoT) applications. Other than operating properly under system uncertainties, CPSs should be secured under unwanted adversaries. To mark such challenges, this paper proposes the solution of secure decentralized robust control for uncertain CPSs under replayed time-delay and false-data injection attacks altogether. Potentially, considered attacks can force the whole system to instability and crash. Three challenges are addressed, and solutions are presented: 1) model nonlinearity and uncertainties, 2) existing simultaneous time-delay and potential false-data injection attacks with skew probability density functions, and 3) requirement to use real-time attack detection. Thus, a novel, robust control method to deal with thwart attacks on a closedloop control system is proposed to provide the system’s trustworthiness. Additionally, novel attack detection methodologies are presented to detect these advanced attacks rapidly based on statistical methods such as Spearman’s correlation coefficient, Neyman-Pearson (NP) error classification, and trend analysis. Ultimately, the proposed novel attack detection and robust control protocol are verified and evaluated in real-time.