Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return.

A zero-sum Markov defender-attacker game for modeling false pricing in smart grids and its solution by multi-agent reinforcement learning

Zio E.
2020-01-01

Abstract

Consumers in smart grids are expected to engage demand-response programs by two-way communication. This makes smart grids vulnerable to cyber attacks. In this paper, we study the false pricing attacks and model the interaction between attackers and defenders using a zero-sum Markov game, where neither player has full knowledge of the game model. A multi-agent reinforcement learning method is used to solve the Markov game and find the Nash Equilibrium policies for both players. An application to a simple radial power distribution system is worked out. The results show that the proposed algorithm can help the players find mixed strategies to maximize their long-term return.
2020
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
978-981112724-3
Demand-response
False pricing attack
Game theory
Multi-agent reinforcement learning
Smart grids
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1160305
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