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This paper focuses on the exponential stability and the resilient state feedback controller design for large‐scale networked control systems under denial‐of‐service attacks. The duration of each denial‐of‐service attack is captured by a logical processor embedded in the controller. The closed‐loop system of periodic sampled‐data control is modelled as an aperiodic sampled‐data control system associated with lower and upper bounds on the duration of the denial‐of‐service attack. Two formal results are demonstrated. The first result is the stability criterion for large‐scale networked control systems under denial‐of‐service attacks obtained by constructing a two‐sided mode‐dependent loop‐based Lyapunov–Krasovskii functional under the action of a prediction‐based controller. The second result presents a criterion based on linear matrix inequalities to design the controller against denial‐of‐service attacks. In particular, iterative algorithms are given for computing the allowable delay upper bound for the system. Finally, the effectiveness of the proposed method is verified by the interconnected power systems of the two areas. This paper focuses on the exponential stability and the resilient state feedback controller design for large‐scale networked control systems under denial‐of‐service attacks. And the effectiveness of the proposed method is verified by the interconnected power systems of the two areas.

Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on agent centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method. Aiming at the reconnaissance task of unmanned vehicle formation under the malicious attack, a security state estimation method based on attack signal reconstruction is proposed. First, the reconstruction of attack signal is transformed into a sparse error correction problem by stacking the measurement information of adjacent vehicles, and is solved by orthogonal matching pursuit (OMP) algorithm. Then, the attack compensation based particle filter is designed to estimate the target state for each vehicle. An information fusion strategy is designed to obtain the final reconnaissance result based on node centrality and the number of attacks on unmanned vehicles. Finally, simulations are provided to illustrate the effectiveness of the proposed method.

As smart manufacturing environments continue to evolve, operational technology systems are increasingly integrated with external networks and cloud-based platforms. However, many manufacturing facilities still use legacy systems running on end-of-support/life operating systems with discontinued security updates. It is difficult to mitigate the cyber threats and risks for these systems using perimeter-based security models that isolate them from other networks. To address these constraints, a Zero Trust-based security architecture tailored for legacy manufacturing environments with practical field applicability is proposed. Our architecture builds upon the six core functions outlined in National Institute of Standards and Technology Cybersecurity Framework 2.0—identify, protect, detect, respond, recover, and govern—adapting them specifically to manufacturing environment security challenges. To achieve this, the architecture combines asset identification, policy-driven access control, secure SMB gateway transfers, automated anomaly detection and response, clean image recovery, and organizational governance procedures. This study validates the effectiveness and scalability of the proposed architecture through scenario-based simulations. When combining the EoSL defense hardening and gateway-based perimeter control, the architecture achieves approximately 99% overall threat suppression and a 98% reduction in critical-asset infection rates, demonstrating its strong resilience and scalability in large-scale legacy OT environments.

This paper investigates strategies for achieving optimal output synchronization of heterogeneous multi‐agent systems in the presence of false data injection attacks. We formulate a performance index with an infinite time horizon using a zero‐sum game framework, treating control input and false data injection attack input as two opposing players. Specifically, the control input's objective is to minimize the performance index, while the false data injection attack input aims to maximize it. Adhering to the optimality principle, we derive the optimal control policy, contingent upon the solution to a related algebraic Riccati equation. Moreover, we propose sufficient conditions that ensure the existence of a solution to the algebraic Riccati equation. Additionally, we have devised a data‐driven reinforcement learning algorithm to seek the solution, and its convergence is assured. Furthermore, it has been demonstrated that the solution to this game corresponds to a Nash equilibrium point. Finally, the validity of the proposed methodology is substantiated through simulation results. This paper explores strategies to achieve optimal output synchronization in heterogeneous multi‐agent systems while facing false data injection attacks. Utilizing a zero‐sum game framework, a performance index is formulated, with control input and attack input as opposing players. The study presents a data‐driven reinforcement learning algorithm to find the Nash equilibrium point, ensuring system stability and robustness, as demonstrated through simulation results.

This study addresses vehicle trajectory tracking control under tri‐modal cyber attacks, encompassing fixed sensor‐to‐controller/controller‐to‐actuator channel attacks in lateral dynamics and sparse multi‐sensor attacks in position tracking. A hybrid fuzzy modeling framework is developed, integrating fuzzy logic inference with Takagi‐Sugeno fuzzy techniques to approximate vehicle dynamics with time‐varying velocity, payload‐dependent mass, and unmeasurable cornering stiffness avoiding the conservatism inherent in conventional linear fractional transformation approaches for cornering stiffness parameterization. A dual‐observer architecture combining an extended state observer and a supervisory fuzzy reduced‐order observer (ESO‐SFRO) is proposed for simultaneous system state reconstruction and tri‐modal attack signal estimation. Based on the estimated states, a cyber‐resilient controller is designed to ensure lateral stability and trajectory tracking accuracy. Experimental validation via CarSim/Simulink co‐simulation demonstrates the proposed ESO‐SFRO based controller exhibits superior dynamic stability and trajectory tracking performance under coupled cyber‐physical disturbances. This study proposes a hybrid fuzzy modeling framework for vehicle trajectory tracking under tri‐modal cyber attacks, integrating Takagi‐Sugeno techniques to handle time‐varying dynamics and unmeasurable cornering stiffness. A dual‐observer architecture (ESO‐SFRO) simultaneously reconstructs system states and estimates attack signals, enabling a cyber‐resilient controller that ensures lateral stability and tracking accuracy. CarSim/Simulink co‐simulation validates the controller's robustness against coupled cyber‐physical disturbances.

This paper presents a novel detection and rerouting mechanism for distributed adaptive platoon control of non‐linear autonomous connected vehicles under denial of service (DoS) attacks. DoS attacks can cause delays or losses of data packets due to blocked communication channels, leading to reducing platoon performance or even collisions among vehicles. To tackle this issue, the proposed mechanism detects and reroutes communication topology depending on the real‐time topology and the number of link failures. Real‐time detection divides the scenario of DoS attacks into three parts. According to the different scenarios, rerouting mechanisms will be utilized. A controller adapted to real‐time variable communication topology is also designed in this scheme. The adjacency matrix of the real‐time communication topology generated by the rerouting mechanism is used to update the controller so that the platoon can remain in a stable state without being affected by DoS attacks. In addition, the sliding mode controller and the observer are designed by solving linear matrix inequalities, and the platoon stability and internal stability are proven. Numerical simulation studies demonstrate that the proposed mechanism and control design can reduce the vehicle state estimate error and platoon‐tracking error to ideal states under DoS attacks. The proposed method solves the problem that the existing methods have not considered the number of link failures and the inability to restore communication when the communication topology is paralyzed.

This article proposes a novel approach for designing a mode‐dependent H∞ $\\mathcal {H}_\\infty$full‐order dynamic filter for a cyber‐physical system (CPS) that is subject to polytopic uncertainties. The CPS operates on an unreliable network that is susceptible to transmission failures and Denial of Service (DoS) attacks. The attackers have limited energy resources, and the duration of the DoS attack is limited to a maximum number of consecutive time instants. The network is modeled after a proposed non‐homogeneous Markov chain whose transition probability matrix may feature uncertain and unknown probabilities, which are dependent on time‐varying parameters. The design conditions for the filter are obtained using parameter‐dependent linear matrix inequalities. The proposed filter is shown to be effective in reducing the impact of DoS attacks and transmission failures on the CPS. Numerical experiments are presented to illustrate the efficacy of the proposed filter design method, demonstrating its ability to mitigate the effects of uncertainties and attacks on the CPS. This paper proposes a non‐homogeneous Markov chain whose transition probability matrix may feature uncertain and unknown probabilities, to consider both the effects of denial of service attacks (DoS) and failure in the communication channels for filter design. The design conditions for the filter are obtained using parameter‐dependent linear matrix inequalities. The proposed filter is shown to be effective in reducing the impact of DoS attacks and transmission failures on the cyber‐physical system.

Research by scientists dealing with sustainable development issues in the aviation industry security focuses on finding solutions that constitute the so-called ‘golden mean’ between appropriate efficiency and high levels of system safety and reliability (including human reliability). The features mentioned above have been repeatedly investigated in various studies, but always individually—to date, no one has proposed a solution indicating the balance point of all the abovementioned features. Here we propose a solution to this research gap: a model for assessing the configuration of airport security control systems. The model allows for the optimal configuration of airport security control systems. The multi-level model validation presented in the article was performed, among others, based on one of the airports in Poland, and showed that the correct configuration of the system can bring energy savings of 913,500 kWh/year in the case of large international airports. Additionally, the article discusses all solutions and modern technologies equipped with devices supporting the passenger and baggage screening process.

This paper aims to analyse the dynamic response of a corrupted spacecraft rendezvous system from the perspective of attacker. The optimal data injection attack problem is formulated by constructing a tradeoff cost function in a quadratic form. First, the optimal attack strategy and associated sufficient condition for its existence are derived similar to optimal control for attacker without being detected. Breaking the assumption in most existing works, the goal of this paper is to explore the optimal attack strategy without knowing system matrices. A model free Q‐learning approach is designed with the application to solve attacker's optimization problem. Critic network and action network are used to adaptive tuning the value and action for attacker in a forward time. For a more practical situation, a model free attack strategy design is implemented only based on measured input/output data. Finally, the simulation results on the spacecraft system are presented to show the effectiveness of the proposed method for model free attack strategy design. This paper aims to analyse the dynamic response of a corrupted spacecraft rendezvous system from the perspective of attacker. Model free Q‐learning approach is designed with the application to solve attacker's optimization problem based on state information. For a more practical situation, a model free attack strategy design is implemented only based on measured input/output data.