Machine Learning‐Based Applications for Cyberattack and Defense in Smart Energy Systems

Smart energy systems are typical cyber-physical systems that integrate with information and communications technology. However, the systems are confronted with cyberattacks due to their increasing dependence on cyberspace. Recently, there has been a noticeable surge in the applications of artificial intelligence (AI) in attack design and defense countermeasures of smart energy systems. Among the various AI techniques, machine learning (ML) stands out as one of the most extensively utilized methods. This chapter focuses on the cybersecurity of smart energy systems. It provides a comprehensive review of the applications of ML in energy system security from four aspects: attack, protection, detection, and mitigation. Specifically, we first give a brief introduction to ML approaches, including unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning. Then, we review existing studies from the four domains and reveal the intrinsic relationships between ML approaches and these domains. At last, based on our investigation, we summarize and highlight the ongoing challenges and future research directions that lie in the critical phases of attack resource optimization, cybersecurity assessment, defense resource scheduling, and recovery strategy planning.