A Review on Improvement in Detection of Cyberattacks Using Artificial Intelligence for the Grid Applications

Abstract

The integration of artificial intelligence and machine learning technologies into critical infrastructures, such as smart grids, has raised significant concerns regarding cybersecurity. This paper explores the dual role of artificial intelligence and machine learning in both enhancing and challenging cybersecurity within smart grid systems. By analysing the current state of-the-art research and technology, the utilisation of artificial intelligence and machine learning to fortify cybersecurity defences while addressing potential vulnerabilities. The the emergence of cyber threats targeting Internet-of-Things-based smart grids is highlighted and solutions to mitigate these risks are proposed. Through a comprehensive review of literature, the efficacy of artificial intelligence driven cybersecurity measures in detecting and preventing cyberattacks are evaluated. Additionally, challenges associated with implementing these solutions in smart grid environments, such as data complexity and computational requirements are taken into account. The findings underscore the critical importance of ongoing research and innovation to ensure the resilience of smart grid cybersecurity.