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.

This article proposes an online solution to address the problem of closed-loop system identification using multiple recursive least squares estimation protocols. Some control systems cannot be analysed in an open-loop form for stability reasons or the requirement for online control system operation. So, it is necessary to identify plant dynamics and controller parameters based on input–output data from the feedback structure. The presented method identifies real-time parameters of plant dynamics and controller parameters by utilising a series of recursive least square estimation algorithms that estimate open-loop data from noisy input–output data measured from the closed-loop feedback structure. The proposed method can effectively identify abrupt variations in both the controller parameters and plant dynamics. This capability makes it valuable for deployment as a supervisory component, enabling the detection of any faults that may arise in operating systems. Mathematical formulations and theorems are developed, and two numerical case studies are presented to examine the feasibility and performance of the presented closed-loop system identification protocol.

This paper investigates the security issue of the data replay attacks on the control systems. The attacker is assumed to interfere with the control system’s process in a steady-state case. The problem is presented as the standard way to attack, which is storing measurements and replaying them in further times to the system. The controller is assumed to be the LQG controller. The main novelty in this paper can be stated as proposing a different attack detection criterion by using the Kullback-Leibler (K-L) divergence method to cover more general control system problems with these attacks and with higher-order dynamics. Also, there exists a packet-dropout feature in transmitting the data as another contribution of the paper. Formulations and numerical simulations prove the effectiveness of the newly proposed attack detection procedure by having a quick response to occurred attacks with various values of the considered packet-dropout coefficient. Although, in previous approaches, 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, these attacks can be stopped with defense strategies in the first moments with the proposed attack detection criterion.

Decentralized control of MIMO system is still the dominating control strategy in industry for its ease in design and maintenance, despite its known shortcomings in performance. In this article, an online decoupling scheme of MIMO processes using an extremum-seeking approach is proposed. By applying the developed method, at first, the system's loop interactions are estimated in real-time using phasor extremum seeking, and then, a pre-compensator matrix is calculated and augmented to the process by minimizing the loop interactions. The presented online decoupling approach is a model-free method, and only the plant's inputs and outputs are analyzed to provide the decentralized control protocol. Numerical results provide the effectiveness of the developed method for a two-input, two-output (TITO) control system.

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.

In this paper, the covariance control algorithm for nonlinear stochastic systems using covariance feedback is studied. Covariance control of nonlinear systems scenario involves the theory of covariance control based on the idea of the covariance feedback. Therefore, the proposed covariance control algorithm is derived for our case, firstly by applying the covariance control method and linear approximation of nonlinear systems, and then it is achieved by adopting this method for a class of nonlinear stochastic systems by using feedback linearization idea and a covariance feedback controller. The effectiveness of the proposed covariance feedback algorithm is studied using numerous simulations concerning different nonlinear case studies.

In this paper, the optimal cyber-attack on remote estimation center in multi nonlinear systems is obtained to study defense strategies in future better. It is assumed that there are F independent nonlinear systems, and each of them has a remote sensor for monitoring. An attacker in the network is considered to exacerbate the specific number of communication channels by generating noise. Because of the capacity limitations in real-world systems, a considered attacker can aggravate at most G of the F communication channels based on its responsibility. The problem is derived as a Markov decision process (MDP) one. Besides, the proof of the existence of an optimal cyber-attack, it is shown that based on the obtained threshold structure of the optimal policy, this problem can be solved for homogeneous models asymptotically, which eases the complexity of computations in real-world applications. In order to check the optimal policy’s performance, numerical results are illustrated.