A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid

Mohammed, SH, Al-Jumaily, A, Singh, MSJ, Jimenez, VPG, Jaber, AS, Hussein, YS, Al-Najjar, MMAK and Al-Jumeily, D (2024) A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid. IEEE Access, 12. pp. 44023-44042.

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Abstract

The Smart Grid is a modern power grid that relies on advanced technologies to provide reliable and sustainable electricity. However, its integration with various communication technologies and IoT devices makes it vulnerable to cyber-attacks. Such attacks can lead to significant damage, economic losses, and public safety hazards. To ensure the security of the smart grid, increasingly strong security solutions are needed. This paper provides a comprehensive analysis of the vulnerabilities of the smart grid and the different approaches for detecting cyber-attacks. It examines the different vulnerabilities of the smart grid, including system vulnerabilities and cyber-attacks, and discusses the vulnerabilities of all its elements. The paper also investigates various approaches for detecting cyber-attacks, including rule-based, signature-based, anomaly detection, and machine learning-based methods, with a focus on their effectiveness and related research. Finally, prospective cybersecurity approaches for the smart grid, such as AI approaches and blockchain, are discussed along with the challenges and future prospects of cyberattacks on the smart grid. The paper's findings can help policymakers and stakeholders make informed decisions about the security of the smart grid and develop effective strategies to protect it from cyber-attacks.

Item Type:	Article
Uncontrolled Keywords:	08 Information and Computing Sciences; 09 Engineering; 10 Technology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science and Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Date of acceptance:	20 February 2024
Date of first compliant Open Access:	29 July 2024
Date Deposited:	29 Jul 2024 10:46
Last Modified:	29 Jul 2024 10:46
DOI or ID number:	10.1109/ACCESS.2024.3370911
URI:	https://ljmu-9.eprints-hosting.org/id/eprint/23807

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