With the popularization and application of cyber-physical systems in many industry and infrastructure fields, the security issue has been a quite important concern. This paper addresses the defense problem against the data tampering attack under the framework of system identification with binary-valued observations. From the perspective of the attacker, it is shown that how to achieve the maximum hit effect with the least attack energy. From the perspective of the defender, a so-called compensation-oriented defense approach is proposed, and the corresponding identification algorithm is designed. The consistency of the algorithm is proved, and the asymptotic normality is obtained, based on which the optimal defense scheme is established. A simulation example is provided to illustrate the effectiveness of the defense algorithm and the main 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.

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.