This paper investigates the security issue of the data replay attacks on control systems with the LQG controller. The attacker tries to store measurements and replay them in further times. The main novelty in this paper is stated as proposing a different attack detection criterion under the existence of a packet-dropout feature in the network by using the Kullback-Leibler divergence method to cover more general problems and with higher-order dynamics. Formulations and numerical simulations prove the effectiveness of the newly proposed attack detection procedure. Unlike previous approaches that the trade-off between attack detection delay or LQG the performance was significant, in this approach it is proved that the difference in this trade-off is not considered in early moments when the attack happens since the attack detection rate is rapid and thus, the attacks can be stopped with defense strategies in the first moments with the proposed attack detection criterion.
Control systems need to be able to operate under uncertainty and especially under attacks. To address such challenges, this paper formulates the solution of robust control for uncertain systems under time-varying and unknown time-delay attacks in cyber-physical systems (CPSs). A novel control method able to deal with thwart time-delay attacks on closed-loop control systems is proposed. Using a descriptor model and an appropriate Lyapunov functional, sufficient conditions for closed-loop stability are derived based on linear matrix inequalities (LMIs). A design procedure is proposed to obtain an optimal state feedback control gain such that the uncertain system can be resistant under an injection time-delay attack with variable delay. Furthermore, various fault detection frameworks are proposed by following the dynamics of the measured data at the system's input and output using statistical analysis such as correlation analysis and K-L (Kullback-Leibler) divergence criteria to detect attack's existence and to prevent possible instability. Finally, an example is provided to evaluate the proposed design method's effectiveness.
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 non-linearity 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 closed-loop 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.
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