Optimal Defense and Control for Cyber-Physical Systems
In this paper, we present a novel representation for cyber-physical systems wherein the states of the cyber system are incorporated into the physical system and vice Next, by using this representation, optimal strategies are derived for the defender and the attacker by using zero-sum game formulation and iterative Q-learning is utilized to obtain the Nash equilibrium. In addition, a Q-learning-based optimal controller is revisited for the physical system with the presence of uncertain dynamics resulting from the cyber system under attacks. The benefit of the learning strategy is that the approach can handle a variety of attacks provided they affect packet losses and delays. Simulation results, on the yaw-channel control of the unmanned aerial vehicle (UAV), show that for the cyber system, both the defender and the attacker gain their largest payoff and for the physical system, the controller maintains the system stable.
H. Niu and J. Sarangapani, "Optimal Defense and Control for Cyber-Physical Systems," Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa), pp. 634-639, Institute of Electrical and Electronics Engineers (IEEE), Dec 2015.
The definitive version is available at https://doi.org/10.1109/SSCI.2015.98
2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 (2015: Dec. 7-10, Cape Town, South Africa)
Electrical and Computer Engineering
Keywords and Phrases
Artificial intelligence; Controllers; Game theory; Unmanned aerial vehicles (UAV); Channel control; Cyber physical systems (CPSs); Learning strategy; Nash equilibria; Optimal controller; Optimal strategies; Physical systems; Uncertain dynamics; Embedded systems
International Standard Book Number (ISBN)
Article - Conference proceedings
© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Dec 2015