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Countering Cybersecurity Threats in Smart Grid Systems Using Machine Learning

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Emerging Trends in Cybersecurity Applications

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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

    A correction has been published.

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Authors and Affiliations

  1. Texas A&M University, Kingsville, TX, USA

    Mais Nijim, Hisham Albataineh, Viswas Kanumuri, Ayush Goyal, Avdesh Mishra & David Hicks

Authors
  1. Mais Nijim
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  2. Hisham Albataineh
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  3. Viswas Kanumuri
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  4. Ayush Goyal
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  5. Avdesh Mishra
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  6. David Hicks
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Corresponding author

Correspondence to Mais Nijim .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Kent Institute Australia, Sydney, NSW, Australia

    Abeer Alsadoon

  3. Ulster University, Ulster, UK

    Cathryn Peoples

  4. EPITA Engineering School, Paris, France

    Nour El Madhoun

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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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  • DOI: https://doi.org/10.1007/978-3-031-09640-2_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09639-6

  • Online ISBN: 978-3-031-09640-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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