In Cyber-Physical Systems security (CPSs), replay attacks disrupt the temporal alignment between inputs and outputs. The dual nonlinearity of Hammerstein system and quantized measurements further increases the difficulty of parameter estimation. This paper investigates the intrusion tolerant (meaning the capable of maintaining performance despite attack interference) identification problem of quantized Hammerstein systems under replay attacks. On the attack side, an optimal attack strategy is formulated by maximizing estimation error under energy constraints. A closed-form solution is derived using quadratic optimization and projection. For the defense design, a sending mechanism based on stochastic intervals and binary detection sequences is proposed. By leveraging the statistical properties of the detection sequence, the mechanism compensates for estimation errors caused by attacks. The system’s nonlinear and quantized structure is reformulated as a solvable nonlinear system, enabling joint estimation of system parameters and attack probabilities. Theoretical analysis demonstrates that the estimators are consistent and asymptotically normal, and an optimal setting for defense factors is presented. Simulation examples validate the effectiveness and robustness of the proposed approach in terms of identification accuracy and attack resistance.

Based on the Stackelberg game theory, this paper studies the optimal consensus control problem of the multi-agent system (MAS) under multiple network attacks. Multiple attackers interfere with the information interaction between agents by intervening in the transmission channel of the multi-agent wireless network, thereby changing the communication topology of the MAS. Under the limited energy of the system and attackers, the energy allocation of transmission channels is used as the performance index, and the importance of the transmission channel to the MAS is used as an auxiliary indicator to establish a Stackelberg game model between the MAS and attackers under incomplete information. By solving the Stackelberg equilibrium solution of the game model, the optimal attack energy allocation scheme and the optimal energy allocation scheme of the system are obtained, respectively. Then, based on the equilibrium solution, the Hamilton-Jacobi-Bellman equation with coupling terms for the MAS under attacks is given. In view of the broad learning system theory, online algorithms that approximate the optimal performance function and the optimal controller are designed respectively, and sufficient conditions are obtained for the proposed optimal controller to ensure the stability and consistency of the MAS. Finally, the effectiveness of the method is verified by a numerical simulation.

The focus of this article is on the consensus problem of the multiagent system (MAS) with binary-valued quantized data under data tampering attacks. First, the properties of data tampering attacks are analyzed, and sufficient conditions are provided under which the attacks can effectively disrupt the consensus algorithm. Subsequently, inspired by the scheme of hierarchical estimation, a multitime scale consensus algorithm is proposed, enabling the MAS to achieve mean square consensus in the presence of tampering attacks. Next, the convergence speed of the consensus algorithm is analyzed, and it is demonstrated that the algorithm designed in this article retains the same upper bound for convergence speed under attacks as the two-time scale consensus algorithm without attacks. Finally, the effectiveness of the proposed method are validated through a signal frequency consensus experiment of the uncrewed aerial vehicles swarm system.

Aiming at the security problem of cyber physical system (CPS), this paper designs and evaluates a tampering attack detection scheme for remote state estimation with quantized observations. When the transmitted information is quantized and the attacker randomly tampers it with a certain probability, we design a detection algorithm with a given error threshold based on the received data. Moreover, we proposes detection performance indexes, leakage detection rate and false detection rate, for the above algorithm, which are used to measure the algorithm performance to discriminate the tampered data, and further analyzes the impact of the attack strategy and the error threshold on performance indexes. Finally, simulations are designed to verify the theoretical results.

This article addresses the security issue against data tampering attacks in the identification of finite impulse response (FIR) systems with binary-valued observations. First, the data tampering rate and estimation error caused by the network attack are derived. From the perspective of the attacker, it is investigated how to achieve the maximum attack effect with the minimum energy. Second, the compensation-type defense scheme is designed. Under this, the identification algorithm is constructed, and its strong convergence is proved. Taking the covariance matrix of the estimation error as the performance index, the optimal defense scheme is given. Finally, the results obtained are verified by simulation example.

Packet loss in block erasure channels creates a randomly switching networked system that impacts control performance significantly. This paper employs safety of highway vehicle platoons as a platform to study such impact. By autonomous inter-vehicle coordination, a platoon can potentially enhance safety, improve highway utility, increase fuel economy, and reduce emission. By comparing different information structures which utilize radar distance sensors and wireless communication channels, we are able to characterize some intrinsic relationships between communication resources and control performance. The findings of this paper provide useful guidelines on communication resource allocations and vehicle coordinations in vehicle safety problems.

This paper investigates the identification of the finite impulse response (FIR) systems with binary-valued observations. Combining with the stochastic gradient algorithm and statistical property of the system noise, a recursive projection algorithm is proposed to estimate the unknown parameters. Under some mild conditions on the a priori knowledge of the unknown parameters and inputs, the algorithm is proved to be convergent in the almost sure and mean square sense. Furthermore, the almost sure and mean square convergence rates of estimation errors are also obtained, and the schemes of selecting the quantization value are provided to ensure such rates. A numerical example is given to demonstrate the effectiveness of the algorithm and the main results obtained.