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.

A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning / Panfili, M; Giuseppi, A; Fiaschetti, A; Al-Jibreen, Hb; Pietrabissa, A; Priscoli, Fd. - (2018), pp. 460-465. (Intervento presentato al convegno 26th Mediterranean Conference on Control and Automation, MED 2018 tenutosi a Zadar; Croatia).

A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning

Panfili, M
;
Giuseppi, A
;
Pietrabissa, A
;
Priscoli, FD
2018

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
26th Mediterranean Conference on Control and Automation, MED 2018
Stochastic Games; Reinforcement Learning; Vulnerability Management; Critical Infrastructure Protection; Composable Security
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Game-Theoretical Approach to Cyber-Security of Critical Infrastructures Based on Multi-Agent Reinforcement Learning / Panfili, M; Giuseppi, A; Fiaschetti, A; Al-Jibreen, Hb; Pietrabissa, A; Priscoli, Fd. - (2018), pp. 460-465. (Intervento presentato al convegno 26th Mediterranean Conference on Control and Automation, MED 2018 tenutosi a Zadar; Croatia).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1331712
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