State estimation for cyber-physical systems with limited communication resources, sensor saturation and denial-of-service attacks

Secure control design for nonlinear cyber-physical systems under DoS, replay, and deception cyber-attacks with multiple transmission channels

This paper introduces the state-/output-feedback control for multi-channel nonlinear cyber-physical systems (CPSs). Many cyber-attacks are considered such as Denial-of-Service (DoS), replay and deception attacks. The deception cyber-attacks can be treated as measurement additive and multiplicative uncertainties. Both time-varying state-dependent and state-independent sensor additive attacks are considered. As DoS attack makes the CPS states unavailable, the standard modeling​ and control methods cannot be applied directly. Alternatively, as attackers in the replay attack re-transmit previous data and prevent the transmission of the more recent data, a delayed model is generated. To deal with these problems, a new observer at the controller side is proposed. It is used to perform two main tasks. The first is to estimate all system states at every time instant. The second is to exclude some unsecured transmitting channels from affecting the system response. Therefore, all attacks in these channels will have no effect on the system response. Using the estimated states, an anti-cyber-attacks state-feedback controller is investigated. Meanwhile, it is verified that the suggested approach certifies the convergence of all the CPSs states under different cyber-attacks. The effectiveness of the proposed secure control approach against different kinds of cyber-attacks is confirmed through two examples with simulation results.

Leader-following consensus for multi-agent systems subject to cyber attacks: Dynamic event-triggered control

This paper addresses the event-triggered consensus problem for multi-agent systems (MASs) under cyber attacks in the leader-following framework. Via introducing a recoverable cyber attack and dynamic event-triggered mechanism (DEM), the centralized and distributed event-triggered controllers are co-designed. Meanwhile, some dynamic triggering criteria are given to ensure the MASs can achieve consensus. The DEM contains time-varying dynamic parameters, which can extend the interevent time interval to avoid Zeno phenomenon. The simulation results verify the effectiveness of the proposed scheme.

A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems

A cyber-physical system (CPS) is the integration of a physical system into the real world and control applications in a computing system, interacting through a communications network. Network technology connecting physical systems and computing systems enables the simultaneous control of many physical systems and provides intelligent applications for them. However, enhancing connectivity leads to extended attack vectors in which attackers can trespass on the network and launch cyber-physical attacks, remotely disrupting the CPS. Therefore, extensive studies into cyber-physical security are being conducted in various domains, such as physical, network, and computing systems. Moreover, large-scale and complex CPSs make it difficult to analyze and detect cyber-physical attacks, and thus, machine learning (ML) techniques have recently been adopted for cyber-physical security. In this survey, we provide an extensive review of the threats and ML-based security designs for CPSs. First, we present a CPS structure that classifies the functions of the CPS into three layers: the physical system, the network, and software applications. Then, we discuss the taxonomy of cyber-physical attacks on each layer, and in particular, we analyze attacks based on the dynamics of the physical system. We review existing studies on detecting cyber-physical attacks with various ML techniques from the perspectives of the physical system, the network, and the computing system. Furthermore, we discuss future research directions for ML-based cyber-physical security research in the context of real-time constraints, resiliency, and dataset generation to learn about the possible attacks.

Event-Based Secure Leader-Following Consensus Control for Multiagent Systems With Multiple Cyber Attacks

This article concentrates on event-based secure leader-following consensus control for multiagent systems (MASs) with multiple cyber attacks, which contain replay attacks and denial-of-service (DoS) attacks. A new multiple cyber-attacks model is first built by considering replay attacks and DoS attacks simultaneously. Different from the existing researches on MASs with a fixed topological graph, the changes of communication topologies caused by DoS attacks are considered for MASs. Besides, an event-triggered mechanism is adopted for mitigating a load of network bandwidth by scheduling the transmission of sampled data. Then, an event-based consensus control protocol is first developed for MASs subjected to multiple cyber attacks. In view of this, by using the Lyapunov stability theory, sufficient conditions are obtained to ensure the mean-square exponential consensus of MASs. Furthermore, the event-based controller gain is derived by solving a set of linear matrix inequalities. Finally, an example is simulated for confirming the effectiveness of the theoretical results.

Neural-Network-Based Adaptive Resilient Dynamic Surface Control Against Unknown Deception Attacks of Uncertain Nonlinear Time-Delay Cyberphysical Systems

A neural-network-based dynamic surface design strategy against sensor and actuator deception attacks is presented to design a delay-independent adaptive resilient control scheme of uncertain nonlinear time-delay cyberphysical systems in the lower triangular form. It is assumed that all nonlinearities, time-varying delays, and sensor and actuator attacks are unknown. In the concerned problem, since the state information measured by sensors is compromised by additional attack signals, the exact state variables are not available for feedback. Thus, a memoryless adaptive resilient control design using compromised state variables is developed by employing the neural-network-based function approximation technique and designing the attack compensator. The resulting control scheme ensures the robust stabilization in the presence of unknown deception attacks and time-varying delays. It is shown from the Lyapunov stability analysis that all closed-loop signals are uniformly ultimately bounded and the stabilization errors converge to an adjustable neighborhood of the origin.

Finite-horizon tracking control for a class of stochastic systems subject to input constraints and hybrid cyber attacks

This paper is concerned with the probabilistic-constrained finite-horizon tracking control problem for a class of stochastic systems subject to randomly occurring hybrid cyber attacks and input constraints. Both the randomly occurring denial-of-service (DOS) attacks and randomly occurring deception attacks are considered in an unified framework. The purpose of the current study is to design an observer-based tracking controller such that: over a finite horizon, (1) the variance of the estimation error is less than certain bound at each time step, (2) the probability of the tracking error falling in certain region should larger than a specified value and the region is minimized at each time step. To achieve those purposes, an improved multi-dimensional Chebyshev inequality method is first utilized to convert the probabilistic constraint to a deterministic one. Then an observer-based tracking control method is designed to estimate the state and design the tracking controller, which are realized through solving a set of recursive linear matrix inequalities. By using the proposed algorithm, the observer and tracking controller gains can be solved out in terms of the solution to a convex optimization problem. A simulation example is finally given to demonstrate the effectiveness and applicability of the proposed method.

Event-Triggered Consensus Control for Multi-Agent Systems Against False Data-Injection Attacks

In this article, the event-triggered security consensus problem is studied for time-varying multiagent systems (MASs) against false data-injection attacks (FDIAs) and parameter uncertainties over a given finite horizon. In the process of information transmission, the malicious attacker tries to inject false signals to destroy consensus by compromising the integrity of measurements and control signals. The randomly occurring stealthy FDIAs on sensors and actuators are modeled by the Bernoulli processes. In order to reduce the unnecessary utilization of communication resources, an event-triggered control mechanism with state-dependent threshold is adopted to update the control input signal. The main objective of this article is to design a controller such that, under randomly occurring FDIAs and admissible parameter uncertainties, the MASs achieve consensus. By utilizing stochastic analysis method, two sufficient criteria are derived to ensure that the prescribed H_∞ consensus performance can be achieved. Then, the desired controller gains are derived by solving recursive linear matrix inequalities. Simulation results are presented to illustrate the effectiveness and applicability of the proposed control method.

Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map

In this paper, we present neuro-fuzzy cognitive map (NFCM) to control a non-holonomic wheeled mobile robot, for both the kinematic control and the dynamic control. For this purpose, the rules for updating the parameters of NFCM used in online training have been extracted. Also, the convergence of the presented approach has been confirmed by means of Lyapunov method. To evaluate the strength and robustness of the proposed model, it has been tested in tracking different circular and square paths. Experimental results indicate that despite the presence of disturbances, the changes of system parameters, and the existence of non-holonomic constraints, our robot has been able to follow challenging paths (e.g. square-shape trajectories) successfully.

Positive observer design for switched positive T-S fuzzy delayed systems with dwell time constraints

In this paper, the positive observer design problem is investigated for switched positive Takagi-Sugeno (T-S) fuzzy delayed systems (SPFDSs) with dwell time constraints. First, by using a clock-dependent co-positive Lyapunov-Krasovskii function (CDCLKF), sufficient conditions are derived for stability of SPFDSs with dwell time. Then, the observer design problem of SPFDSs is separately considered for the measurable and unmeasurable premise variable cases. On the basis of the obtained stability results, sufficient conditions are presented to check the existence of observers for SPFDSs with measurable and unmeasurable premise variables. Through minimizing the system matrix norm of the error dynamics, the optimized observer gains for SPFDSs with unmeasurable premise variables are achieved, and the errors between the system states and their estimations are reduced. All the clock-dependent conditions are relaxed into sum of squares (SOS) program via the SOS approximation approach. At last, two examples are given to show the validity of our results.