Abstract
The traditional grid has been gradually replaced with novel smart grid systems. Smart grids integrate the traditional grid systems with ICT solutions that turn on communication from both ends, enhancing system reliability and efficiency. However, they generate massive data that make them a target for many attacks that can disrupt service and cause property damage, privacy issues, money theft, and loss of data leak. Machine learning technologies are used to detect anomalies and provide on-demand responses, among other security services on the grid. In this chapter, we discuss smart grid systems and the application of machine learning in countering cyber threats. In the following sections, the chapter identifies the value of smart grid systems, their conceptual model, machine learning, and types of machine learning and reviews existing literature, cyber threats facing smart grid systems, and applications of machine learning algorithms in securing the grid.
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19 July 2023
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Nijim, M., Albataineh, H., Kanumuri, V., Goyal, A., Mishra, A., Hicks, D. (2023). Countering Cybersecurity Threats in Smart Grid Systems Using Machine Learning. In: Daimi, K., Alsadoon, A., Peoples, C., El Madhoun, N. (eds) Emerging Trends in Cybersecurity Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-09640-2_14
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