A Hierarchical Game Approach to the Coupled Resilient Control of CPS against Denial-of-Service Attack

In this paper, the resilient control problem has been studied for cyber-physical systems (CPSs) under the Denial-of-Service (DoS) attack. The term resilience is interpreted as the ability to be robust to the physical layer external disturbance and defending against cyber layer DoS attacks. The overall resilient control system is described by a hierarchical game, where the cyber security issue is modeled as a zerosum matrix game, and physical H∞ minimax control problem is described by a zero-sum dynamic game. In virtue of the reinforcement learning method, the defense/attack policy in the cyber layer can be obtained. Additionally, the physical layer control strategy can be obtained by using the dynamical programming method. The criteria to judge whether a cyber system is capable of protecting the underlying control system are provided as well. Finally, the proposed method is verified by a numerical example.

[1]  George Kesidis,et al.  Denial-of-service attack-detection techniques , 2006, IEEE Internet Computing.

[2]  Y. Weinsberg,et al.  High performance string matching algorithm for a network intrusion prevention system (NIPS) , 2006, 2006 Workshop on High Performance Switching and Routing.

[3]  Tamer Basar,et al.  H∞-Optimal Control and Related , 1991 .

[4]  Zidong Wang,et al.  Near-Optimal Resilient Control Strategy Design for State-Saturated Networked Systems Under Stochastic Communication Protocol , 2019, IEEE Transactions on Cybernetics.

[5]  Jeff S. Shamma,et al.  Dynamic fictitious play, dynamic gradient play, and distributed convergence to Nash equilibria , 2005, IEEE Transactions on Automatic Control.

[6]  Petros A. Ioannou,et al.  Adaptive steering control for uncertain ship dynamics and stability analysis , 2013, Autom..

[7]  John Y. Hung,et al.  Denial of service attacks on network-based control systems: impact and mitigation , 2005, IEEE Transactions on Industrial Informatics.

[8]  Tilman Börgers,et al.  Learning Through Reinforcement and Replicator Dynamics , 1997 .

[9]  Quanyan Zhu,et al.  Resilient control of cyber-physical systems against Denial-of-Service attacks , 2013, 2013 6th International Symposium on Resilient Control Systems (ISRCS).

[10]  Lei Guo,et al.  Active Disturbance Rejection Control for the Ranger Neutral Buoyancy Vehicle: A Delta Operator Approach , 2017, IEEE Transactions on Industrial Electronics.

[11]  Vinay M. Igure,et al.  Security issues in SCADA networks , 2006, Comput. Secur..

[12]  Tamer Basar,et al.  Optimal control of LTI systems over unreliable communication links , 2006, Autom..

[13]  Tansu Alpcan,et al.  Network Security , 2010 .

[14]  David Geer.  Security of critical control systems sparks concern , 2006, Computer.

[15]  Quanyan Zhu,et al.  Heterogeneous learning in zero-sum stochastic games with incomplete information , 2011, 49th IEEE Conference on Decision and Control (CDC).

[16]  David I. Gertman,et al.  Resilient control systems: Next generation design research , 2009, 2009 2nd Conference on Human System Interactions.

[17]  Quanyan Zhu,et al.  Robust and resilient control design for cyber-physical systems with an application to power systems , 2011, IEEE Conference on Decision and Control and European Control Conference.

[18]  Lei Guo,et al.  Resilient Control of Networked Control System Under DoS Attacks: A Unified Game Approach , 2016, IEEE Transactions on Industrial Informatics.