A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning. Panfili, M., Giuseppi, A., Fiaschetti, A., Al-Jibreen, H., Pietrabissa, A., & Delli Priscoli, F. 2018. doi abstract bibtex 1 download This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy. © 2018 IEEE.
@CONFERENCE{Panfili2018460,
author={Panfili, M. and Giuseppi, A. and Fiaschetti, A. and Al-Jibreen, H.B. and Pietrabissa, A. and Delli Priscoli, F.},
title={A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning},
journal={MED 2018 - 26th Mediterranean Conference on Control and Automation},
year={2018},
pages={460-465},
doi={10.1109/MED.2018.8442695},
art_number={8442695},
abstract={This paper presents a control strategy for Cyber-Physical System defense developed in the framework of the European Project ATENA, that concerns Critical Infrastructure (CI) protection. The aim of the controller is to find the optimal security configuration, in terms of countermeasures to implement, in order to address the system vulnerabilities. The attack/defense problem is modeled as a multi-agent general sum game, where the aim of the defender is to prevent the most damage possible by finding an optimal trade-off between prevention actions and their costs. The problem is solved utilizing Reinforcement Learning and simulation results provide a proof of the proposed concept, showing how the defender of the protected CI is able to minimize the damage caused by his her opponents by finding the Nash equilibrium of the game in the zero-sum variant, and, in a more general scenario, by driving the attacker in the position where the damage she/he can cause to the infrastructure is lower than the cost it has to sustain to enforce her/his attack strategy. © 2018 IEEE.},
author_keywords={Composable Security; Critical Infrastructure Protection; Reinforcement Learning; Stochastic Games; Vulnerability Management},
keywords={Costs; Economic and social effects; Embedded systems; Game theory; Multi agent systems; Public works; Reinforcement learning; Stochastic systems, Composable; Control strategies; Critical infrastructure protection; Multi-agent reinforcement learning; Stochastic game; System vulnerability; Theoretical approach; Vulnerability management, Critical infrastructures},
document_type={Conference Paper},
}
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