Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution
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Published:2023-03-30
Issue:1
Volume:11
Page:143-147
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ISSN:2347-470X
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Container-title:International Journal of Electrical and Electronics Research
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language:en
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Short-container-title:IJEER
Author:
Mohamed Nachaat1, Oubelaid Adel2, Almazrouei Saif khameis3
Affiliation:
1. Rabdan Academy, (Homeland Security Department), Abu Dhabi, UAE 2. Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria 3. Ministry of Interior, (Smart Security Systems Department), UAE
Abstract
The integration of artificial intelligence (AI) and the Internet of Things (IoT) in the power generation and distribution industry presents opportunities and challenges, particularly in the area of cybersecurity. Previous studies have explored the potential of AI to enhance cybersecurity in power systems, but limitations in terms of sample size and scope have hindered a comprehensive understanding of the current state of the field. To address this gap, this paper presents a systematic literature review of 30 papers that analyzes and categorizes relevant research based on their focus on threats, solutions, and future trends. The results indicate that 30 articles provide evidence supporting the use of AI and machine learning techniques to significantly enhance cybersecurity in the power sector. However, the study also highlights the need for continuous monitoring, threat intelligence, and risk management to stay ahead of evolving threats. Notably, this paper provides novel insights into the use of cybersecurity measures, blockchain technology, and awareness of the impact of AI in the power sector, with 90% of organizations using cybersecurity measures, 50% employing blockchain technology, 20% experiencing a cyberattack, and 60% being aware of the impact of AI. The study's limitations include a lack of detailed information on the organizations studied, such as their size and location, and the absence of a standardized approach to data collection across the selected papers. Nonetheless, this paper offers a valuable contribution to the field of AI and cybersecurity in the power industry by providing a comprehensive overview of the current state of research and identifying key areas for further investigation.
Publisher
FOREX Publication
Subject
Electrical and Electronic Engineering,Engineering (miscellaneous)
Reference30 articles.
1. Habibi, M. R., Baghaee, H. R., Dragičević, T., & Blaabjerg, F. (2020). Detection of false data injection cyber-attacks in DC microgrids based on recurrent neural networks. IEEE Journal of Emerging and Selected Topics in Power Electronics, 9(5), 5294-5310. 2. Lv, Z., Han, Y., Singh, A. K., Manogaran, G., & Lv, H. (2020). Trustworthiness in industrial IoT systems based on artificial intelligence. IEEE Transactions on Industrial Informatics, 17(2), 1496-1504. 3. Rana, M. M., Bo, R., & Abdelhadi, A. (2020). Distributed grid state estimation under cyber attacks using optimal filter and Bayesian approach. IEEE Systems Journal, 15(2), 1970-1978. 4. Goudarzi, A., Ghayoor, F., Waseem, M., Fahad, S., & Traore, I. (2022). A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook. Energies, 15(19), 6984. 5. Sadan, N., & Renz, B. (2020, October). New DER Communications Platform Enables DERMS and Conforms with IEEE 1547–2018 Requirements. In 2020 IEEE/PES Transmission and Distribution Conference and Exposition (T & D) (pp. 1-5). IEEE.
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