Decentralized Adaptive Fuzzy Secure Control for Nonlinear Uncertain Interconnected Systems Against Intermittent DoS Attacks

Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

Asynchronous Control for Interval Type-2 Fuzzy Nonhomogeneous Markov Jump Systems Against Successive DoS Attacks

This article is concerned with the problem of asynchronous control for Interval Type-2 (IT2) fuzzy nonhomogeneous Markov jump systems against successive denial-of-service (DoS) attacks. The system and the controller are assumed to be connected through a communication channel subject to malicious attacks. The maximum number and probability distribution of successive attacks are considered. Under the imperfect premise matching, a fuzzy asynchronous controller is constructed by the hidden Markov model. By means of the introduced transmission delay, a delay closed-loop system is constructed, where the stochastic description of the delay depends on the statistical characteristic of successive attacks. Then stability criteria together are derived in the form of linear matrix inequalities by the Lyapunov functional approach, as well as the condition on the existence of the fuzzy controller. Finally, the feasibility and effectiveness of the presented control scheme are demonstrated by simulation results.

Finite-horizon optimal secure tracking control under denial-of-service attacks

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber-physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end.

Attack-Resilient Distributed Nash Equilibrium Seeking of Uncertain Multiagent Systems Over Unreliable Communication Networks

This article investigates the distributed Nash equilibrium (NE) seeking problem of uncertain multiagent systems in unreliable communication networks. In this problem, the action of each agent is subject to a class of nonlinear systems with uncertain dynamics, and the communication network among agents will be affected by the nonperiodic denial of service (DoS) attacks. Note that, in this insecure network environment, the existence of DoS attacks will directly destroy the connectivity of the network, which leads to performance degradation or even failure of the most existing distributed NE seeking algorithms. To address this problem, we propose a two-stage distributed NE seeking strategy, including the attack-resilient distributed NE estimator and the neuroadaptive tracking controller. The estimator based on the projection subgradient method and the consensus protocol can converge exponentially to virtual NE against DoS attacks. Then, the neuroadaptive tracking controller is designed for uncertain multiagent systems with the output of the estimator as the reference signal such that the actual action of all agents can reach NE. Based on the Lyapunov stability theory and improved average dwell time automaton, the stability of the estimator and the controller is proven, and all signals in the closed-loop system are uniformly bounded. Numerical examples are presented to verify the effectiveness of the proposed strategy.

Attack-resilient fault detection for interconnected systems under DoS attack

In light of the expanding cyber-space applications, the imperative consideration of cyber-attack ramifications on system security is evident. This paper presents a resilient dynamic event-triggered fault detection scheme for a class of nonlinear interconnected systems subjected to denial of service (DoS) attacks. To counteract multifaceted threats, the co-design challenge involving switched-type fault detection filters and a resilient dynamic event-triggered transmission mechanism is addressed. In the design phase of the filters, the frequency information of the signal is considered comprehensively and linear solvable conditions ensuring desired augment system performance are delineated. Through a series of comparative simulation experiments, the findings support the conclusion that the proposed attack-tolerant fault detection mechanism not only conserves network resources but also demonstrates superior detection capabilities for specific frequency fault signals.

Observer-Based Adaptive NN Security Control for Switched Nonlinear Systems Against DoS Attacks: An ADT Approach

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

Adaptive tracking control of two-wheeled mobile robots under Denial-of-Service attacks

This paper focuses on the tracking control problem for the two-wheeled mobile robot (TWMR) with unknown parameters. The robot collects its own states from the networked positioning system, which is subject to Denial-of-Service (DoS) attacks. To handle the uncertainties in the robot model and mitigate the attack effects on the system performance, parameter estimators with the projection operator technique are introduced. Then, an adaptive tracking controller is designed by adopting the backstepping technique. Correspondingly, a stability condition is derived, which guarantees that all the closed-loop signals are semi-globally uniformly bounded and tracking errors can converge to an adjustable compact set. The stability condition also reveals the relationships among the attack durations, design parameters and tracking errors, which can be utilized to guide the choice of design parameters. Experimental results are provided to validate the effectiveness of the proposed control scheme.

Event-Triggered and Self-Triggered L∞ Control for Markov Jump Stochastic Nonlinear Systems Under DoS Attacks

This article investigates event-triggered and self-triggered L_∞ control problems for the Markov jump stochastic nonlinear systems subject to denial-of-service (DoS) attacks. When attacks prevent system devices from obtaining valid information over networks, a new switched model with unstable subsystems is constructed to characterize the effect of DoS attacks. On the basis of the switched model, a multiple Lyapunov function method is utilized and a set of sufficient conditions incorporating the event-triggering scheme (ETS) and restriction of DoS attacks are provided to preserve L_∞ performance. In particular, considering that ETS based on mathematical expectation is difficult to be implemented on a practical platform, a self-triggering scheme (STS) without mathematical expectation is presented. Meanwhile, to avoid the Zeno behavior resulted from general exogenous disturbance, a positive lower bound is fixed in STS in advance. In addition, the exponent parameters are designed in STS to reduce triggering frequency. Based on the STS, the mean-square asymptotical stability and almost sure exponential stability are both discussed when the system is in the absence of exogenous disturbance. Finally, two examples are given to substantiate the effectiveness of the proposed method.

Composite Finite-Time Resilient Control for Cyber-Physical Systems Subject to Actuator Attacks

Cyber-physical systems (CPSs) seamlessly integrate communication, computing, and control, thus exhibiting tight coupling of their cyber space with the physical world and human intervention. Forming the basis of future smart services, they play an important role in the era of Industry 4.0. However, CPSs also suffer from increasing cyber attacks due to their connections to the Internet. This article investigates resilient control for a class of CPSs subject to actuator attacks, which intentionally manipulate control commands from controllers to actuators. In our study, the supertwisting sliding-mode algorithm is adopted to construct a finite-time converging extended state observer (ESO) for estimating the state and uncertainty of the system in the presence of actuator attacks. Then, for the attacked system, a finite-time converging resilient controller is designed based on the proposed ESO. It integrates global fast terminal sliding-mode and prescribed performance control. Finally, an industrial CPS, permanent magnet synchronous motor control system, is investigated to demonstrate the effectiveness of the composite resilient control strategy presented in this article.

Synchronization of Complex Dynamical Networks Subject to DoS Attacks: An Improved Coding-Decoding Protocol

This article investigates the synchronization of communication-constrained complex dynamic networks subject to malicious attacks. An observer-based controller is designed by virtue of the bounded encode sequence derived from an improved coding-decoding communication protocol. Moreover, taking the security of data transmission into consideration, the denial-of-service attacks with the frequency and duration characterized by the average dwell-time constraint are introduced into data communication, and their influence on the coder string is analyzed explicitly. Thereafter, by imposing reasonable restrictions on the transmission protocol and the occurrence of attacks, the boundedness of coding intervals can be obtained. Since the precision of data is generally limited, it may lead to the situation that the signal to be encoded overflows the coding interval such that it results in the unavailability of the developed coding scheme. To cope with this problem, a dynamic variable is introduced to the design of the protocol. Subsequently, based on the Lyapunov stability theory, sufficient conditions for ensuring the input-to-state stability of the synchronization error systems under the communication-constrained condition and malicious attacks are presented. The validity of the developed method is finally verified by a simulation example of chaotic networks.

Event-Triggered Resilient L∞ Control for Markov Jump Systems Subject to Denial-of-Service Jamming Attacks

In this article, the event-triggered resilient L_∞ control problem is concerned for the Markov jump systems in the presence of denial-of-service (DoS) jamming attacks. First, a fixed lower bound-based event-triggering scheme (ETS) is presented in order to avoid the Zeno problem caused by exogenous disturbance. Second, when DoS jamming attacks are involved, the transmitted data are blocked and the old control input is kept by using the zero-order holder (ZOH). On the basis of this process, the effect of DoS attacks on ETS is further discussed. Next, by utilizing the state-feedback controller and multiple Lyapunov functions method, some criteria incorporating the restriction of DoS jamming attacks are proposed to guarantee the L_∞ control performance of the event-triggered Markov closed-loop jump system. In particular, the bounded transition rates rather than the exact ones are taken into account. That is appropriate for the practical environment in which transition rates of the Markov process are difficult to measure accurately. Correspondingly, some criteria are proposed to obtain state-feedback gains and event-triggering parameters simultaneously. Finally, we provide two examples to show the effectiveness of the proposed method.

Adaptive NN Finite-Time Resilient Control for Nonlinear Time-Delay Systems With Unknown False Data Injection and Actuator Faults

This article considers neural network (NN)-based adaptive finite-time resilient control problem for a class of nonlinear time-delay systems with unknown fault data injection attacks and actuator faults. In the procedure of recursive design, a coordinate transformation and a modified fractional-order command-filtered (FOCF) backstepping technique are incorporated to handle the unknown false data injection attacks and overcome the issue of "explosion of complexity" caused by repeatedly taking derivatives for virtual control laws. The theoretical analysis proves that the developed resilient controller can guarantee the finite-time stability of the closed-loop system (CLS) and the stabilization errors converge to an adjustable neighborhood of zero. The foremost contributions of this work include: 1) by means of a modified FOCF technique, the adaptive resilient control problem of more general nonlinear time-delay systems with unknown cyberattacks and actuator faults is first considered; 2) different from most of the existing results, the commonly used assumptions on the sign of attack weight and prior knowledge of actuator faults are fully removed in this article. Finally, two simulation examples are given to demonstrate the effectiveness of the developed control scheme.

Learning-Based Distributed Resilient Fault-Tolerant Control Method for Heterogeneous MASs Under Unknown Leader Dynamic

In this article, we consider the distributed fault-tolerant resilient consensus problem for heterogeneous multiagent systems (MASs) under both physical failures and network denial-of-service (DoS) attacks. Different from the existing consensus results, the dynamic model of the leader is unknown for all followers in this article. To learn this unknown dynamic model under the influence of DoS attacks, a distributed resilient learning algorithm is proposed by using the idea of data-driven. Based on the learned dynamic model of the leader, a distributed resilient estimator is designed for each agent to estimate the states of the leader. Then, a new adaptive fault-tolerant resilient controller is designed to resist the effect of physical failures and network DoS attacks. Moreover, it is shown that the consensus can be achieved with the proposed learning-based fault-tolerant resilient control method. Finally, a simulation example is provided to show the effectiveness of the proposed method.

Edge-Based Decentralized Adaptive Pinning Synchronization of Complex Networks Under Link Attacks

This article studies the pinning synchronization problem with edge-based decentralized adaptive schemes under link attacks. The link attacks considered here are a class of malicious attacks to break links between neighboring nodes in complex networks. In such an insecure network environment, two kinds of edge-based decentralized adaptive update strategies (synchronous and asynchronous) on coupling strengths and gains are designed to realize the security synchronization of complex networks. Moreover, by virtue of the edge pinning technique, the corresponding secure synchronization problem is considered under the case where only a small fraction of coupling strengths and gains is updated. These designed adaptive strategies do not require any global information, and therefore, the obtained results in this article are developed in a fully decentralized framework. Finally, a numerical example is provided to verify the availability of the achieved theoretical outcomes.

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.

Observer-Based Security Control for Interconnected Semi-Markovian Jump Systems With Unknown Transition Probabilities

This article investigates the issue of observer-based security control for the interconnected semi-Markovian jump systems with completely unknown and uncertain bounded transition probabilities (TPs). Considering the limited bandwidth of communication network in each subsystem, an adaptive event-triggered mechanism (AETM) is developed to relieve more network burden than the conventional event-triggered mechanism (ETM), where the designed adaptive law can dynamically adjust the triggering threshold. In addition, two Bernoulli distributed variables are utilized to describe the influence of denial-of-service (DoS) attacks and false-data injection (FDI) attacks in the proposed observer-based security control strategy. Moreover, some sufficient criterions are derived for the stochastic stability with an H_∞ attenuation level of augmented systems. Meanwhile, the observer and controller gain matrices can be attained simultaneously with the help of linear matrix inequalities (LMIs). Finally, we provide a practical example to demonstrate the effectiveness of theoretical results.

Resilient Event-Triggered Distributed State Estimation for Nonlinear Systems Against DoS Attacks

This article investigates the resilient event-triggered (ET) distributed state estimation problem for nonlinear systems under denial-of-service (DoS) attacks. Different from the existing results mainly considering linear or specified nonlinear systems, more general nonlinear systems are considered in this study. Moreover, the considered DoS attacks are able to compromise different communication links among estimators independently. In this context, by resorting to the techniques of incremental homogeneity, a nonlinear ET distributed estimation scheme is designed to estimate the states and regulate the data transmission. Under this scheme, the resilient state estimation is achieved by employing a multimode switching estimator, and the problem of efficiency loss of the ET mechanism caused by DoS attacks is solved by designing a dynamic trigger threshold with switched update laws. Then, based on the decay rates of the Lyapunov function corresponding to different communication modes, sufficient conditions are given to guarantee the stability of the estimation error system under DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed method.

Secure predictor-based neural dynamic surface control of nonlinear cyber-physical systems against sensor and actuator attacks

This paper addresses a secure predictor-based neural dynamic surface control (SPNDSC) issue for a cyber-physical system in a nontriangular form suffering from both sensor and actuator deception attacks. To avoid the algebraic loop problem, only partial states are employed as input vectors of neural networks (NNs) for approximating unknown dynamics, and compensation terms are further developed to offset approximation errors from NNs. With introduction of nonlinear gain functions and attack compensators, adverse effects of an intelligent adversary are alleviated effectively. Furthermore, we present stability analysis and prove the ultimate boundedness of all signals in the closed-loop system. The effectiveness of the proposed control strategy is illustrated by two examples.

Cooperative Fault-Tolerant Control for a Class of Nonlinear MASs by Resilient Learning Approach

In this article, a learning-based resilient fault-tolerant control method is proposed for a class of uncertain nonlinear multiagent systems (MASs) to enhance the security and reliability against denial-of-service (DoS) attacks and actuator faults. With the framework of cooperative output regulation, the developed algorithm consists of designing a distributed resilient observer and a decentralized fault-tolerant controller. Specifically, by using the data-driven method, an online resilient learning algorithm is first presented to learn the unknown exosystem matrix in the presence of DoS attacks. Then, a distributed resilient observer is proposed working against DoS attacks. In addition, based on the developed observer, a decentralized adaptive fault-tolerant controller is designed to compensate for actuator faults. Moreover, the convergence of error systems is shown by using the Lyapunov stability theory. The effectiveness of our result is examined by a simulation example.

A Hierarchical Security Control Framework of Nonlinear CPSs Against DoS Attacks With Application to Power Sharing of AC Microgrids

In this article, we investigate the distributed resilient observers-based decentralized adaptive control problem for cyber-physical systems (CPSs) with time-varying reference trajectory under denial-of-service (DoS) attacks. The considered CPSs are modeled as a class of nonlinear multi-input uncertain multiagent systems, which can be used to model an AC microgrid system consisting of distributed generators. When the communication to a subsystem from one of its neighbors is attacked by a DoS attack, the transmitted information is unavailable and the existing distributed adaptive methods used to estimate the bound of the n th-order derivative of the reference trajectory become nonapplicable. To overcome this difficulty, we first design a new distributed estimator for each subsystem to ensure that the magnitude of the state of the estimator is larger than the bound of the n th-order derivative of the reference trajectory after a finite time. By employing the estimator state, a distributed observer with a switching mechanism is proposed. Then, a new block backstepping-based decentralized adaptive controller is developed. Based on the DoS communication duration property, convex design conditions of observer parameters are derived with the Lebesgue integral theory and the average dwell time method. It is proved that the output tracking errors will approach a compact set with the developed method. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the power sharing problem for AC microgrids.

Event-Triggered Distributed State Estimation for Cyber-Physical Systems Under DoS Attacks

This article investigates the event-triggered distributed state estimation problem for a class of cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. First, an observer-based event-triggered transmission scheme is proposed to improve the transmission efficiency, and the corresponding distributed Kalman filter is designed to estimate the system states. Under the collective observability condition, a relationship between estimation error covariance, attack intensity, and transmission efficiency is established by utilizing the covariance intersection fusion method and the property of matrix congruent transformation rank. The important features that distinguish our work from others are that the considered DoS attacks compromise each channel independently and do not have to satisfy the probabilistic property of the packet loss process. Furthermore, an event-triggered communication scheme is considered to improve the utilization of network resources between filters, and a sufficient condition for the parameter design is given which takes into account the influence of DoS attacks. Finally, simulation results are provided to verify the effectiveness of the proposed methods.

Observer-Based Fixed-Time Secure Tracking Consensus for Networked High-Order Multiagent Systems Against DoS Attacks

This article studies the secure tracking consensus problem of nonlinear multiagent systems (MASs) against denial-of-service (DoS) attacks. Two types of DoS attacks, i.e., connectivity-maintained attacks and connectivity-broken attacks, are considered. The resulting topologies caused by DoS attacks may destabilize the consensus performance of MASs. Especially under connectivity-broken attacks, the connectivity between agents is destroyed. To deal with these difficulties, a novel defense strategy consisting of distributed observation and decentralized control is proposed. First, a distributed fixed-time observer (DFTO) is prepared for the case of connectivity-maintained attacks, which can quickly and accurately estimate the leader's information for each follower. Besides, the adverse impact of DoS attacks is completely eliminated. Furthermore, to cope with the problem arising from connectivity-broken attacks, by using an online algorithm of updating label information, an improved resilient DFTO (RDFTO) is further developed, which can preserve those followers having directed paths from the leader to quickly and accurately estimate the leader's information, without being affected by DoS attacks. The developed DFTO and RDFTO have successfully eliminated or weakened the adverse effects caused by DoS attacks. Subsequently, based on the proposed DFTO/RDFTO with the power integrator technique, a fixed-time controller is finally constructed, which realizes the desired transient performance of consensus tracking in the finite-time interval. The effectiveness of the proposed defense strategy is verified by stability analysis and simulation examples.

Supervisory Nonlinear State Observers for Adversarial Sparse Attacks

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results.

Optimal Stealth Attack Strategy Design for Linear Cyber-Physical Systems

This article studies the problem of the optimal stealth attack strategy design for linear cyber-physical systems (CPSs). Virtual systems that reflect the attacker's target are constructed, and a linear attack model with varying gains is designed based on the virtual models. Unlike the existing optimal stealth attack strategies that are designed based on sufficient conditions, necessary and sufficient conditions are, respectively, established to achieve the optimal attack performance while maintaining stealth in virtue of the solvability of certain coupled recursive Riccati difference equations (RDEs). Under those conditions, an optimal stealth attack strategy is constructed by an offline algorithm. A simulation example is applied to verify the effectiveness of the presented technical scheme.

Stability Analysis for Cyber-Physical Systems Under Denial-of-Service Attacks

This article investigates the stability analysis problem for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. Based on the real-time data characterizing the suffered DoS attack, a necessary and sufficient condition for the closed-loop stability in the presence of DoS attacks is provided. Besides, by transforming stability analysis the system under DoS into stability analysis of an auxiliary system, novel sufficient conditions for the closed-loop stability, which can be verified more easily than the necessary and sufficient condition, are provided. Based on the proposed conditions, an online stability monitoring strategy, which triggers an alarm if the closed-loop stability is not guaranteed, is proposed. Finally, two examples are given to illustrate the effectiveness of the proposed strategy.

Stochastic model predictive control framework for resilient cyber-physical systems: review and perspectives

In the era of Industrial 4.0, the next-generation control system regards the cyber-physical system (CPS) as the core ingredient thanks to the comprehensive integration of physical systems, online computation, networking and control. A reliable, stable and resilient CPS should pledge robustness and safety. A significant concern in CPS development arises from security issues since the CPS is vulnerable to physical constraints, ubiquitous uncertainties and malicious cyber attacks. The integration of the stochastic model predictive control (MPC) framework and the resilient mechanism is a possible approach to guarantee robustness in the presence of stochastic uncertainties and enable resilience against cyber attacks. This review paper aims to offer a detailed overview of existing stochastic MPC algorithms and their CPS applications. More specifically, we first review existing stochastic MPC algorithms for both linear and nonlinear systems subject to probabilistic constraints. We then discuss how to extend the stochastic MPC framework to incorporate resilience mechanisms for constrained CPS under various malicious attacks. Finally, we present an architectural stochastic MPC-based framework for resilient CPS and identify future research challenges. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Cooperative Output-Feedback Secure Control of Distributed Linear Cyber-Physical Systems Resist Intermittent DoS Attacks

This article studies a cooperative output-feedback secure control problem for distributed cyber-physical systems over an unreliable communication interaction, which is to achieve coordination tracking in the presence of intermittent denial-of-service (DoS) attacks. Under the switching communication network environment, first, a distributed secure control method for each subsystem is proposed via neighborhood information, which includes the local state estimator and cooperative resilient controller. Second, based on the topology-dependent Lyapunov function approach, the design conditions of secure control protocol are derived such that cooperative tracking errors are uniformly ultimately bounded. Interestingly, by exploiting the topology-allocation-dependent average dwell-time (TADADT) technique, the stability analysis of closed-loop error dynamics is presented, and the proposed coordination design conditions can relax time constraints on interaction topology switching. Finally, two numerical examples are presented to demonstrate the effectiveness of the theoretical results.

Trust-based fault detection and robust fault-tolerant control of uncertain cyber-physical systems against time-delay injection attacks

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.

Dissipativity-Based Sliding-Mode Control of Cyber-Physical Systems Under Denial-of-Service Attacks

In this article, we investigate the problem of the dissipativity-based resilient sliding-mode control design of cyber-physical systems with the occurrence of denial-of-service (DoS) attacks. First, we analyze the physical layer operating without DoS attacks to ensure the input-to-state practical stability (ISpS). The upper bound of the sample-data rate in this situation can be identified synchronously. Next, for systems under DoS attacks, we present the following results: 1) combined with reasonable hypotheses of DoS attacks, the ISpS as well as dissipativity of the underlying system can be guaranteed; 2) the upper bound of the sample-data rate in the presence of DoS attacks can be derived; and 3) the sliding-mode controller is synthesized to achieve the desired goals in a finite time. Finally, two examples are given to illustrate the applicability of our theoretical derivation.

MAS-Based Distributed Resilient Control for a Class of Cyber-Physical Systems With Communication Delays Under DoS Attacks

In this article, we investigate the distributed resilient control problem for a class of cyber-physical systems with communication delays under denial-of-service (DoS) attacks. In contrast to the previous DoS attacks results based on multiagent systems (MASs), a new distributed resilient control approach is proposed for more general heterogeneous linear MASs with nonuniform communication delays. Two types of sampled-based observers are, respectively, proposed. Namely, adaptive distributed observers are designed by introducing a buffer mechanism to eliminate the heterogeneous behavior caused by communication delays while adaptive distributed resilient observers are designed by introducing resilient mechanisms to resist the DoS attacks. Furthermore, a time-varying sampling period sequence is provided to prevent the attacker from identifying the sampling period of the system. Based on the developed resilient observers, a controller is developed. It is proved that the considered problem can be solved by the developed method. Finally, a numerical example is given to illustrate the effectiveness of the obtained result.

Cooperative control for cyber-physical multi-agent networked control systems with unknown false data-injection and replay cyber-attacks

The paper discusses the cooperative tracking problem of partially known cyber-physical multi-agent networked systems. In this system, there exist two cascaded chances for cyber-attacks. The local agent is of networked system type that is subjected to unknown false data-injection and replay cyber-attacks that are dissimilar in the sensor-controller and the controller-actuator network parts. The communication between any two agents, if they are connected, is accomplished via a communication network that is subjected to false data-injection cyber-attacks. The problem of the existing two cascaded chances for cyber-attacks is solved in three steps. First, with partially known system parameters and unknown false data-injection and replay cyber-attacks, the state estimates of all the local followers are evaluated by designing local adaptive observers. Second, a new technique is designed to compensate for the unmatched terms that result from the use of local adaptive observers. After that, distributed adaptive leader-follower security controllers are proposed based on the local estimated information in addition to the infected arrived information from the neighbors. Meanwhile, it is verified that the suggested security control method guarantees that all states of the followers under the considered cyber-attacks follow the given leader asymptotically. The efficacy of the developed adaptive leader-follower security controllers is verified via an illustrative example.

An Analysis on Optimal Attack Schedule Based on Channel Hopping Scheme in Cyber-Physical Systems

In this paper, we investigate the issue of security on the remote state estimation in cyber-physical systems (CPSs), where a wireless sensor utilizes the channel hopping scheme to transmit the data to the remote estimator over multiple channels in the presence of periodic denial-of-service attacks. Assume that the jammer can interfere with a subset of channels at each attack time in active period. For an energy-constraint jammer, the problem of how to select the number of channels at each attack time to maximally deteriorate the CPS performance is investigated. Based on the index of average estimation error, we introduce two different attack strategies, which include selecting identical number of channels and unequal number of channels at each attack time, and further show theoretically that the attack effect by selecting unequal number of channels is better than that of selecting identical number of channels. By formulating the problem of selecting the number of channels as integer programming problems, we present the corresponding algorithm to approximate the optimal attack schedule for both cases. The numerical results are presented to validate the theoretical results and the effectiveness of the proposed algorithms.

Observer-Based Control for Cyber-Physical Systems Under Denial-of-Service With a Decentralized Event-Triggered Scheme

This article concerns the problem of observer-based event-triggered control for cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. In contrast to the existing studies where DoS attacks on different channels are the same, the considered attacks compromise each channel independently. Correspondingly, a decentralized event-triggered scheme is adopted based on the tradeoff between the transmission efficiency and tolerable attack intensity with guarantees on the closed-loop stability. Inspired by the Lyapunov theory for switched systems, the proposed stabilization criteria reveals a link between the tolerable attack intensity and the event-triggering parameters. An example is finally provided to illustrate the effectiveness of the proposed approaches.

Resilient Observer-Based Control for Cyber-Physical Systems With Multiple Transmission Channels Under Denial-of-Service

This paper investigates the resilient control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS). First, a set of partial observers is designed to estimate partial states corresponding to different channels. Second, by combining the proposed partial observers, the finite-time observer technique and a switching scheme, a modified finite-time partial observer-based controller is proposed to stabilize the system in the presence of DoS. Compared with the existing results for the CPSs with multiple transmission channels, the computational burden of designing observer-based controller is reduced, and the resilience against DoS is improved by adopting the proposed finite-time partial observers. Especially, besides stability, the proposed resilient observer-based controller also provides better disturbance rejection performance.

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.

Observer Design of Discrete-Time Fuzzy Systems Based on an Alterable Weights Method

This paper proposes an improvement on observer design of discrete-time fuzzy systems based on an alterable weights method. Different from the recent result, a more effective ranking-based switching mechanism is developed by introducing a bank of alterable weights for the sake of making use of the size difference information of the normalized fuzzy weighting functions more freely than before. Therefore, a positive result can be provided in this paper, that is, less conservative conditions of designing feasible fuzzy observers can be obtained than those existing results, while the computational cost of designing feasible fuzzy observers is even less than the up-to-date one. Finally, two numerical examples are given to show the progressiveness of the proposed method.