Trust-based fault detection and robust fault-tolerant control of uncertain cyber-physical systems against time-delay injection attacks

Abstract

Control systems need to be able to operate under uncertainty and especially under attacks. To address such challenges, this paper formulates the solution of robust control for uncertain systems under time-varying and unknown time-delay attacks in cyber-physical systems (CPSs). A novel control method able to deal with thwart time-delay attacks on closed-loop control systems is proposed. Using a descriptor model and an appropriate Lyapunov functional, sufficient conditions for closed-loop stability are derived based on linear matrix inequalities (LMIs). A design procedure is proposed to obtain an optimal state feedback control gain such that the uncertain system can be resistant under an injection time-delay attack with variable delay. Furthermore, various fault detection frameworks are proposed by following the dynamics of the measured data at the system's input and output using statistical analysis such as correlation analysis and K-L (Kullback-Leibler) divergence criteria to detect attack's existence and to prevent possible instability. Finally, an example is provided to evaluate the proposed design method's effectiveness.

Attack detection and fault‐tolerant control of interconnected cyber‐physical systems against simultaneous replayed time‐delay and false‐data injection attacks

“…Specifically, some types of deception attacks occur regularly in industrial CPSs. These common attacks include false-data injection attacks in which the generated false data (noise) from the attacker is injected into the communication network to decrease the system's data authenticity, replay attacks in which the previous time data packets are stored and sent repeatedly to prevent the subsystems from reaching the steady-state phase [10], and time-delay attacks in which a delay parameter is injected to the system to cause instability in system's operation [11,12]. Recently, authors in [13] developed a model-free reinforcement learning algorithm to control CPSs under time-delay attacks from a robust perspective.…”

“…The K–L measure is effective in detecting attacks with Gaussian distributions in higher‐order linear/non‐linear CPSs [26]. Additionally, correlation analysis is reviewed as another attack detection framework in [12] and its performance is compared with the K–L divergence criterion in real‐time false‐data detection scenarios. During our research on CPSs, we observed that by considering non‐Gaussian signals injected into the system in the form of time delays or injected false data, the system's performance degraded significantly while the malicious signals remained stealthy.…”