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In this paper, the H∞ control problem for a class of linear time-varying cyber-physical system (CPS) under randomly occurring hybrid attacks in a finite horizon is investigated. The hybrid attacks, including denial of service (DoS) attacks on both sensor-to-controller and controller-to-actuator communication channels and false data injection (FDI) attacks on sensors and actuators, aim to destroy the measurement data and control data in order to endanger the functionality of the closed-loop system. The purpose of this paper is to study the relationship between the attack injected signals and the controlled output, and to design the output feedback controller gains so that the H∞ performance of the closed-loop system is guaranteed over a given finite horizon, meanwhile, the impact of attack signals in the worst case on the linear quadratic performance can be reduced. In order to solve the above problems, both the methods of stochastic analysis and completing squares are utilized to establish the sufficient conditions for the existence of the desired controller, and a finite-horizon controller design algorithm is presented by solving two coupled backward recursive Riccati difference equations (RDEs) subject to some scheduled conditions. At last, the numerical simulation and experimental results are given out to demonstrate the effectiveness of the proposed approach.

This paper focuses on the data-driven optimal attack strategy against state estimation in cyber-physical systems (CPSs). Different from the research on attack strategies of specific attack types, the proposed attack strategy addresses the optimal selection of attacked targets, which can combine with different attack types and produce greater threats to CPS. In particular, a causality analysis (CA) on the measurement data is first proposed to evaluate the significance of nodes (sensor groups) and help the implementation of the optimal node attack, since the system topology and parameters are not available to adversaries. On the one hand, a multivariate transfer entropy and several data preprocessing methods are employed to complete the CA between sensor groups qualitatively. On the other hand, three new indexes, e.g., driver degree, are defined to complete the CA quantitatively. Moreover, the theoretical basis for the proposed node attack is provided, in which the superiority of the node attack is proven from the view of observability. Finally, the case studies on the smart grid are illustrated to verify the superiority of the proposed attack strategy.