Game-Theoretic Model for Optimal Cyber-Attack Defensive Decision-Making in Cyber-Physical Power Systems

Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs...

Full description

Saved in:
Bibliographic Details
Published in2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2) pp. 2359 - 2364
Main Authors Yao, Pengchao, Hao, Weijie, Yan, Bingjing, Yang, Tao, Wang, Jinming, Yang, Qiang
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.10.2021
Subjects
CPPS
game theory
Measurement
Micromechanical devices
Nash equilibrium
Power systems
proactive security decision
realistic testbed
Reinforcement learning
System integration
System performance
Online AccessGet full text

Cover

More Information
Summary:Cyber-Physical Power Systems (CPPSs) currently face an increasing number of security attacks and lack methods for optimal proactive security decisions to defend the attacks. This paper proposed an optimal defensive method based on game theory to minimize the system performance deterioration of CPPSs under cyberspace attacks. The reinforcement learning algorithmic solution is used to obtain the Nash equilibrium and a set of metrics of system vulnerabilities are adopted to quantify the cost of defense against cyber-attacks. The minimax-Q algorithm is utilized to obtain the optimal defense strategy without the availability of the attacker's information. The proposed solution is assessed through experiments based on a realistic power generation microsystem testbed and the numerical results confirmed its effectiveness.
DOI:10.1109/EI252483.2021.9712960