Enhancing Cybersecurity in Smart Grids through Machine Learning-Based Intrusion Detection Systems

Abstract

Smart grids represent the modernization of electrical grids by integrating advanced communication and information technology. However, their interconnected nature and reliance on digital infrastructure make them vulnerable to cyber-attacks. Enhancing cybersecurity in smart grids is paramount to ensure reliability and safety. This paper explores the use of Machine Learning-Based Intrusion Detection Systems (ML-IDS) as a solution to enhance the cybersecurity of smart grids. It examines the current cybersecurity challenges in smart grids, the fundamentals of intrusion detection systems, the integration of machine learning in IDS, and the effectiveness of ML-IDS in mitigating cyber threats.