Hacking Distributed Energy Resource Power Plant Infrastructure Using Reinforcement Learning

Abstract

Advancements in Artificial Intelligence (AI) are enabling adversaries to more efficiently penetrate and navigate networks, posing new risks to critical infrastructure such as the electric power grid. This study evaluates three attack strategies—AI-based, Brute-Force, and Random—within the Network Attack Simulator (NASim), a synthetic environment designed for cybersecurity testing. The AI-based methods include Deep Q-Network (DQN) and Deep State-Action-Reward-State-Action (SARSA) reinforcement learning algorithms. Training results show that both AI approaches effectively learn to conduct subnet scans, service/process scans, and privilege escalation attacks to gain root access. During testing, AI agents completed their objectives in fewer than 28 actions, while Brute-Force and Random methods required over 200 actions. These findings demonstrate AI’s efficiency and potential to automate the launch of cyberattacks targeting Distributed Energy Resources (DERs), offering a baseline for future research targeting real-world networks.