How Machine Learning Can Support Cyberattack Detection in Smart Grids

Abstract

This chapter addresses the application of machine learning algorithms to detect attacks against smart grids. Smart grids are the result of a long process of transformation that power systems have been through, relying on Information and Communication Technology (ICT) to improve their monitoring and control. Although an objective of this convergence of power systems and ICT is to increase their reliability, the dependency on information technology has brought new cybersecurity vulnerabilities to this scenario. Therefore, developing new cybersecurity measures for smart grids is a key factor in their success. One of these measures is attack detection, which allows the timely mitigation of attacks with the aim of limiting possible damages to the targets. As machine learning algorithms have been widely applied as powerful tools to support the design of cybersecurity solutions in multiple areas, they also have huge potential for addressing the new challenges that smart grids pose. With this as the foundational perspective, this study starts by presenting an overview of smart grids, followed by possible attacks. After this discussion, we examine the background concepts for attack detection and machine learning. Then, we discuss the existing solutions, showing in detail how they address the particularities of smart grids and their attack types using machine learning algorithms. This is supplemented by a discussion of the open issues in the use of machine learning for smart grid attack detection, followed by some future research directions.