This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber-physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{\infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{\infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.

Connected autonomous vehicles (CAVs) platoons rely on V2V communication and onboard sensing to maintain safe inter-vehicle spacing, yet cyberattacks on links and sensors can inject false data and destabilize platoon control. This paper proposes a hierarchical Secure Tiny Model Predictive Control (Secure TinyMPC) framework for real-time resilient platooning. A verifiable secret sharing (VSS)-based security layer distributes state shares across communication links to reconstruct trusted states and to support distributed, online detection and estimation of communication and radar attacks. A low-computation TinyMPC control layer then uses these trusted states to rapidly compute control inputs suitable for resource-limited onboard hardware. Simulation studies under diverse communication attacks and radar measurement tampering demonstrate accurate spacing regulation, fast speed convergence, and effective real-time distributed attack detection.

This paper proposes a zonotopic secure estimation framework for discrete-time cyber-physical systems (CPSs) subject to unknown-but-bounded (UBB) disturbances and false data injection (FDI) attacks. A decentralized dynamic event-triggered mechanism (DETM) is developed, allowing each sensor to adapt its triggering threshold using local output deviations, thereby reducing communication while preserving estimation accuracy. Subsequently, a zonotopic interval observer is developed to estimate the system state under DETM. The observer propagates zonotopic error bounds and is designed via LMIs to ensure stability and l1 performance. Furthermore, a zonotope-based attack reconstruction approach is formulated. The attack signal is conservatively enclosed in a residual-based zonotope, and a threshold test is used to isolate attacked channels. Finally, simulation results confirm that the method reduces communication significantly while maintaining reliable estimation, validating its use in resource-constrained CPSs.

In this paper, resilient distributed set-based estimation is investigated for cyber-physical systems (CPSs) with unknown-but-bounded (UBB) noises which are characterized by constrained polynomial zonotopes (CPZs). Both generalized intersection of CPZs and diffusion strategy are developed to calculate the measurement update and obtain the estimation set. When the system is vulnerable to malicious false-data-injection (FDI) attacks, the encoding-decoding approach is proposed to preserve privacy, which can also be utilized to improve the attack detection rate. After detecting attacks, the corresponding resilient estimation algorithm is provided to alleviate the impact introduced by attacks. Finally, numerical simulations are provided to illustrate the validity of the presented approaches.

This article discusses the security concerns related to networked systems, where the sensor sends the local estimate to the remote estimator, which may be attacked. Traditionally, in the remote state estimation with the innovation or raw measurement case, denial of service (DoS) and false data injection (FDI) attacks are investigated thoroughly. Notably, for remote state estimation with local estimate cases considered in this article, most existing works consider DoS attacks but not FDI attacks, negatively affecting remote state estimation performance. Furthermore, current detection mechanisms encounter challenges when identifying such attacks due to the unavailable innovation or raw measurement. As such, we study FDI attacks under this framework and provide the corresponding secure analysis using a dual-perspective approach. Specifically, we propose a detector to detect such attacks using the prior information extracted from the remote estimator. Then, we analyze the existence of stealthy attacks and characterize the corresponding performance evaluation for the remote estimation under such attacks. Following this, we construct the optimal attack scheme, maximizing the expected average and terminal estimation error covariances, respectively. To reduce the above vulnerability, we develop a co-design transmission strategy and offer an analytical detection performance evaluation under different attack scenarios. Finally, simulations are provided to illustrate the proposed results.

This article explores the security control in a remote control system driven by local and remote controllers. By utilizing the information of the local controller (namely, local information, including its mean and error variance), we propose a new actuator-side detector that can prevent performance degradation caused by attacks on the remote control signal, which is transmitted to the actuator through wireless communication. Besides, it can also overcome the difficulties when a standard Kullback–Leibler divergence detector fails to detect such attacks before the control signal is input into the system due to the unavailability of innovation$z_{k}$or measurement$y_{k}$. Subsequently, we characterize the corresponding impacts of different attack patterns on the estimation performance under the proposed detector. Based on this, we offer a compensation mechanism to improve the performance of the remote estimator under the attack. Finally, simulations are provided to illustrate the developed results.

Cyber–physical systems (CPSs) driven by a local controller and a remote controller have been gaining significant research interest in recent years due to its application scenarios in practice, such as unmanned aerial vehicles (UAVs). In this article, we consider the security issue in the remote control system with a local controller under stealthy attacks. Under this framework, one controller is designed locally based on the limited measurements collected by a local sensor, and the other controller is designed remotely and is transmitted to the actuator through a wireless communication channel, which may suffer malicious attacks due to its openness. To defend attacks on remote control signal, the K–L divergence-based detector or$\chi ^{2}$detector is often adopted. However, there may be attackers adopting stealthy attacks, which can bypass such detectors. Therefore, we analyze the existence of such attacks, and analytically characterize the worst-estimation performance degradation induced by the remote control signal attack. Further, we construct the optimal attack signal to achieve the upper bound on the estimation performance degradation. In addition, we also give countermeasures against such stealthy attacks. Simulations are provided to illustrate the proposed results.

This article is concerned with the attack detection issue for a class of nonlinear cyber–physical systems (CPSs) with unknown-but-bounded (UBB) noises. The nonlinear system is linearized by utilizing first-order Taylor expansion with Lagrangian remainder, and an observer based on zonotopic sets is proposed to estimate the system states. Then, a novel encoding–decoding strategy (EDS) is provided to improve the attack detection rate by selecting appropriate parameters. In order to alleviate the impact introduced by malicious attacks, a countermeasure is taken into account when an attack is detected by the abnormal detector. Finally, the hardware-in-the-loop (HIL) simulation is provided to illustrate the effectiveness of the proposed results.

This article studies a security issue in remote distributed consensus estimation where sensors transmit their measurements to remote estimators via a wireless communication network. The relative entropy is utilized as a stealthiness metric to detect whether the data transmitted through the wireless network are attacked. The performance degradation induced by an attacker that attempts to be stealthy or undetected is analyzed, and the corresponding false-data attack strategy is characterized, which can achieve the maximal integrated mean-square error (IMSE). Finally, the tradeoff between the performance degradation and attack stealthiness level is evaluated through an example.

This article studies the attack detection problem of cyber–physical systems (CPSs) with the event-triggered (ET) mechanism. A switching-based moving-target defense (MTD) strategy is developed to detect malicious false-data-injection (FDI) attacks, which is characterized by the average dwell-time (ADT) property. Even if attacks may remain stealthy when the MTD mechanism is adopted, the impact of these stealthy attacks on the system performance is relatively small when the switching signal is unknown to the adversary. In order to reduce the cost of data transmission, an ET mechanism is added into the system. Furthermore, we prove that there exists a lower bound of the execution interval, which indicates that the Zeno phenomenon is excluded. Finally, numerical examples are employed to illustrate the effectiveness of the main results.

This article proposes a novel attack detection approach based on zonotopes for linear parameter-varying (LPV) systems with unknown-but-bounded (UBB) noises. The following three types of attacks are considered: 1) denial-of-service (DoS) attacks; 2) replay attacks (RAs); and 3) false-data-injection (FDI) attacks. In order to reduce the conservativeness, a free-weighting matrix is introduced, which can be computed by solving an optimization problem. Moreover, the radius of the intersection zonotope can be guaranteed to be limited as well. Furthermore, it is not necessary to acquire the knowledge about the specific type of attack in advance. Finally, a numerical example is given to illustrate the validity of the given method.

In this paper, the problems of state estimation are investigated for switched linear systems with average dwell time (ADT) switching in both continuous-time and discrete-time contexts. First, a set of mode-dependent Luenberger-type observers is designed subject to the ADT switching that is synchronous with the switching of the estimated systems. Then, a more practical case of the delayed observers is also considered, which implies that the switching of the multiple-mode observer to be designed has a lag to the switching of the estimated systems. In this case, the asynchronous switching signals are combined as a preliminary attempt, upon which sufficient conditions for the existence of the Luenberger-type observers and the corresponding ADT switching are formulated in terms of a set of linear matrix inequalities. Finally, the proposed approaches are applied to the state estimation of electronic circuits to demonstrate the effectiveness and applicability.