Biblio

This paper investigates an asymptotically stable fault tolerant control (FTC) method for nonlinear continuous-time systems (NCTS) with actuator failures via differential game theory (DGT). Based on DGT, the FTC problem can be regarded as a two-player differential game problem with control player and fault player, which is solved by utilizing adaptive dynamic programming technique. Using a critic-only neural network, the cost function is approximated to obtain the solution of the Hamilton-Jacobi-Isaacs equation (HJIE). Then, the FTC strategy can be obtained based on the saddle point of HJIE, and ensures the satisfactory control performance for NCTS. Furthermore, the closed-loop NCTS can be guaranteed to be asymptotically stable, rather than ultimately uniformly bounded in corresponding existing methods. Finally, a simulation example is provided to verify the safe and reliable fault tolerance performance of the designed control method.

Ubiquitous Electric Internet of Things (UEIoT) is the next generation electrical energy networks. The distributed and open structure of UEIoT is weak and vulnerable to security threats. To solve the security problem of UEIoT terminal, in this paper, the interaction between smart terminals and the malicious attackers in UEIoT as a differential game is investigated. A complex decision-making process and interactions between the smart terminal and attackers are analyzed. Through derivation and analysis of the model, an algorithm for the optimal defense strategy of UEIoT is designed. The results lay a theoretical foundation, which can support UEIoT make a dynamic strategy to improve the defensive ability.

Aiming at the shortcomings of the traditional network active security threat model that cannot continuously control the threat process, a network active security threat model based on offensive and defensive differential game is constructed. The attack and defense differential game theory is used to define the parameters of the network active security threat model, on this basis, the network security target is determined, the network active security threat is identified by the attack defense differential equation, and finally the network active security threat is quantitatively evaluated, thus construction of network active security threat model based on offensive and defensive differential game is completed. The experimental results show that compared with the traditional network active security threat model, the proposed model is more feasible in the attack and defense control of the network active security threat process, and can achieve the ideal application effect.

Abstract. Multi-agent cyber-physical systems (CPSs) are ubiquitous in modern infrastructure systems, including the future smart grid, transportation networks, and public health systems. Security of these systems are critical for normal operation of our society. In this paper, we focus on physical layer resilient control of these systems subject to cyber attacks and malicious behaviors of physical agents. We establish a cross-layer system model for the investigation of cross-layer coupling and performance interdependencies for CPSs. In addition, we study a twosystem synchronization problem in which one is a malicious agent who intends to mislead the entire system behavior through physical layer interactions. Feedback Nash equilibrium is used as the solution concept for the distributed control in the multi-agent system environment. We corroborate our results with numerical examples, which show the performance interdependencies between two CPSs through cyber and physical interactions.

Wireless sensor networks (WSNs) are prone to propagating malware because of special characteristics of sensor nodes. Considering the fact that sensor nodes periodically enter sleep mode to save energy, we develop traditional epidemic theory and construct a malware propagation model consisting of seven states. We formulate differential equations to represent the dynamics between states. We view the decision-making problem between system and malware as an optimal control problem; therefore, we formulate a malware-defense differential game in which the system can dynamically choose its strategies to minimize the overall cost whereas the malware intelligently varies its strategies over time to maximize this cost. We prove the existence of the saddle-point in the game. Further, we attain optimal dynamic strategies for the system and malware, which are bang-bang controls that can be conveniently operated and are suitable for sensor nodes. Experiments identify factors that influence the propagation of malware. We also determine that optimal dynamic strategies can reduce the overall cost to a certain extent and can suppress the malware propagation. These results support a theoretical foundation to limit malware in WSNs.