Intelligent Active Defense Methods for Mitigating Penetration Attacks on Power Grid Buffer Networks

Abstract

With its flexibility and active defense capabilities, the power grid buffer network has attracted widespread attention as a novel means of power grid defense. This article proposes an intelligent active defense method specifically designed to mitigate penetration attacks on power grid buffer networks. In this method, attackers typically employ intelligent penetration attacks based on reinforcement learning, which model the penetration process as a Markov decision process. Attackers continuously train themselves through trial and error to optimize their penetration paths, thus enhancing their attack capabilities. To prevent malicious exploitation of intelligent penetration attacks, the power grid buffer network introduces a deceptive defense method aimed at countering attack strategies based on reinforcement learning. This method first gathers necessary information (state, action, reward) during the construction of the attack model by attackers. It then generates deceptive actions through state dimension inversion and confuses attackers by flipping reward value signs, thereby implementing deceptive defense at the early, middle, and late stages of penetration attacks on the power grid buffer network. Finally, this article conducts simulation experiments to compare the defensive effectiveness of the proposed method in three stages of the power grid buffer network’s defense against intelligent penetration attacks. The experimental results demonstrate that the proposed method reduces the success rate of intelligent penetration attacks based on reinforcement learning.