Learning-based Secure Control for Multi-channel Networked Systems under Smart Attacks

Yu，Yi1; Liu，Guo Ping1; Hu，Wenshan2

2022

10.1109/TIE.2022.3203680

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS   Impact Factor & Quartile

0278-0046

1557-9948

This paper is concerned with the security control of a class of discrete-time linear cyber-physical systems (CPSs) subject to denial-of-service (DoS) attacks. To enhance the inherent resistance of the CPS against damage and attacks, a multi-channel network is employed for remote information interaction between the ingredients of the system. In this way, a complex interaction process will be formed between the signal sender and the smart malicious adversary. Specifically, in the context of using the multi-channel network, the malicious adversary has to maximize the attack success probability within its energy constraint. As a counterpart, the system tries to mitigate the negative impact of such attacks on CPS control performance. For the purpose of designing a control strategy to cope with the attacks, this interaction process is formulated as a zero-sum stochastic game, while the Nash equilibrium solution of this problem is found with the help of the proposed learning algorithm, and the optimal mixed strategies for both attackers and defenders are derived. Further, for the CPS driven by the obtained decision-making strategies, a Kalman filter-based active dynamic output feedback resilient controller is proposed. Finally, the effectiveness of the developed optimal defense strategies and the resilient controller is demonstrated by extensive case studies on the servo motor experimental platform.

DoS attacks dynamic output feedback secure control energy-depleting jamming attacks Games Heuristic algorithms Interference kalman filtering Nash Q-learning Networked control system Q-learning Resilience Security Signal to noise ratio

EI

English

First

20223812754919

Controllers ; Decision making ; Embedded systems ; Feedback ; Game theory ; Heuristic algorithms ; Kalman filters ; Learning algorithms ; Network security ; Networked control systems ; Optimization ; Reinforcement learning ; Signal to noise ratio ; Stochastic systems

Information Theory and Signal Processing:716.1 ; Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1 ; Artificial Intelligence:723.4 ; Machine Learning:723.4.2 ; Control Systems:731.1 ; Control System Applications:731.2 ; Control Equipment:732.1 ; Legal Aspects:902.3 ; Management:912.2 ; Optimization Techniques:921.5 ; Probability Theory:922.1 ; Systems Science:961

ENGINEERING

2-s2.0-85137906769

Scopus

1.Center for Control Science and Technology, Southern University of Science and Technology, Shenzhen, China
2.Department of Artificial Intelligence and Automation, Wuhan University, Wuhan, China

Yu，Yi,Liu，Guo Ping,Hu，Wenshan. Learning-based Secure Control for Multi-channel Networked Systems under Smart Attacks[J]. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,2022,PP(99):1-11.

Yu，Yi,Liu，Guo Ping,&Hu，Wenshan.(2022).Learning-based Secure Control for Multi-channel Networked Systems under Smart Attacks.IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,PP(99),1-11.

Yu，Yi,et al."Learning-based Secure Control for Multi-channel Networked Systems under Smart Attacks".IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS PP.99(2022):1-11.