FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-110120

Review Article |

Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution

Author(s): Nachaat Mohamed1*, Adel Oubelaid2 and Saif khameis Almazrouei3

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 11, Issue 1

Publisher : FOREX Publication

Published : 15 March 2023

e-ISSN : 2347-470X

Page(s) : 143-147

DOI: https://doi.org/10.37391/IJEER.110120

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.

Keywords: AI, Cyber Security, IoT 3, Power Generation, Threats, Cyber Attack




Nachaat Mohamed*, Rabdan Academy, (Homeland Security Department), Abu Dhabi, UAE; Email: eng.cne1@gmail.com

Adel Oubelaid, Laboratoire de Technologie Industrielle et de l’Information, Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria; Email: adel.oubelaid@univ-bejaia.dz

Saif khameis Almazrouei, Ministry of Interior, (Smart Security Systems Department), UAE; Email: salmazrouei@moi.gov.ae

    [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. [Cross Ref]
    [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. [Cross Ref]
    [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. [Cross Ref]
    [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. [Cross Ref]
    [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. [Cross Ref]
    [6] Okampo, E. J., Nwulu, N., & Bokoro, P. N. (2022). Optimal Placement and Operation of FACTS Technologies in a Cyber-Physical Power System: Critical Review and Future Outlook. Sustainability, 14(13), 7707. [Cross Ref]
    [7] Mao, D., Ding, L., Zhang, C., Rao, H., & Yan, G. (2021, August). Multi-Agent Reinforcement Learning-based Distributed Economic Dispatch Considering Network attacks and Uncertain Costs. In 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA) (pp. 469-474). IEEE. [Cross Ref]
    [8] Mohamed, N. (2022, December). Importance of Artificial Intelligence in Neural Network through using MediaPipe. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology (pp. 1207-1215). IEEE. [Cross Ref]
    [9] Mohammadi, E., Alizadeh, M., Asgarimoghaddam, M., Wang, X., & Simões, M. G. (2022). A review on application of artificial intelligence techniques in microgrids. IEEE Journal of Emerging and Selected Topics in Industrial Electronics. [Cross Ref]
    [10] Otoum, Safa, and Hussein T. Mouftah. "Enabling trustworthiness in sustainable energy infrastructure through blockchain and AI-assisted solutions." IEEE Wireless Communications 28, no. 6 (2021): 19-25. [Cross Ref]
    [11] de Brito, I. B., & de Sousa Jr, R. T. (2022). Development of an open-source testbed based on the modbus protocol for cybersecurity analysis of nuclear power plants. Applied Sciences, 12(15), 7942. [Cross Ref]
    [12] Hosseini, M. M., & Parvania, M. (2021). Artificial intelligence for resilience enhancement of power distribution systems. The Electricity Journal, 34(1), 106880. [Cross Ref]
    [13] Zhang, D., Feng, G., Shi, Y., & Srinivasan, D. (2021). Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances. IEEE/CAA Journal of Automatica Sinica, 8(2), 319-333. [Cross Ref]
    [14] Esenogho, E., Djouani, K., & Kurien, A. M. (2022). Integrating artificial intelligence Internet of Things and 5G for next-generation smartgrid: A survey of trends challenges and prospect. IEEE Access, 10, 4794-4831. [Cross Ref]
    [15] Himdi, T., Ishaque, M., & Ikram, M. J. (2022, March). Cyber security challenges in distributed energy resources for smart cities. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 788-792). IEEE. [Cross Ref]
    [16] Khalel, S. I., & Khudher, S. M. (2022, March). Cyber-Attacks Risk Mitigation on Power System via Artificial Intelligence Technique. In 2022 9th International Conference on Electrical and Electronics Engineering (ICEEE) (pp. 117-122). IEEE. [Cross Ref]
    [17] Aldossary, L. A., Ali, M., & Alasaadi, A. (2021, September). Securing SCADA systems against cyber-attacks using artificial intelligence. In 2021 international conference on innovation and intelligence for informatics, computing, and technologies (3ICT) (pp. 739-745). IEEE. [Cross Ref]
    [18] Abir, S. A. A., Anwar, A., Choi, J., & Kayes, A. S. M. (2021). Iot-enabled smart energy grid: Applications and challenges. IEEE access, 9, 50961-50981. [Cross Ref]
    [19] Feng, C., Liang, B., Li, Z., Liu, W., & Wen, F. (2022). Peer-to-peer energy trading under network constraints based on generalized fast dual ascent. IEEE Transactions on Smart Grid. [Cross Ref]
    [20] Kumar, V., & Gupta, C. P. (2021, September). Cyber security issue in smart grid. In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 1-9). IEEE. [Cross Ref]
    [21] Oubelaid, A., Mohamed, N., Taib, N., Rekioua, T., Bajaj, M., Parashar, D., & Blazek, V. (2022, December). Robust Controllers Design and Performance Investigation of a Vector Controlled Electric Vehicle. In 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT) (pp. 1-6). IEEE. [Cross Ref]
    [22] Lopez, J., Rubio, J. E., & Alcaraz, C. (2021). Digital twins for intelligent authorization in the B5G-enabled smart grid. IEEE Wireless Communications, 28(2), 48-55. [Cross Ref]
    [23] Otoum, S., Al Ridhawi, I., & Mouftah, H. (2022). A federated learning and blockchain-enabled sustainable energy-trade at the edge: A framework for industry 4.0. IEEE Internet of Things Journal. [Cross Ref]
    [24] Acosta, M. R. C., Ahmed, S., Garcia, C. E., & Koo, I. (2020). Extremely randomized trees-based scheme for stealthy cyber-attack detection in smart grid networks. IEEE access, 8, 19921-19933. [Cross Ref]
    [25] Mohamed, N., Kumar, K. S., Sharma, S., Kumar, R. D., Mehta, S., & Mishra, I. (2022). Wireless Sensor Network Security with the Probability Based Neighbourhood Estimation. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 231-235.
    [26] Mohamed, N., & Belaton, B. (2021). SBI model for the detection of advanced persistent threat based on strange behavior of using credential dumping technique. IEEE Access, 9, 42919-42932. [Cross Ref]
    [27] Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. [Cross Ref]
    [28] Mohamed, N. (2022). Study of bypassing Microsoft Windows Security using the MITRE CALDERA framework. F1000Research, 11(422), 422. [Cross Ref]
    [29] Mohamed, N., Almazrouei, S. K., Oubelaid, A., Elsisi, M., ElHalawany, B. M., & Ghoneim, S. S. (2022). Air-Gapped Networks: Exfiltration without Privilege Escalation for Military and Police Units. Wireless Communications and Mobile Computing, 2022. [Cross Ref]
    [30] Mohamed, N., Awasthi, A., Kulkarni, N., Thota, S., Singh, M., & Dhole, S. V. (2022). Decision Tree Based Data Pruning with the Estimation of Oversampling Attributes for the Secure Communication in IOT. International Journal of Intelligent Systems and Applications in Engineering, 10(2s), 212-216.

Nachaat Mohamed, Adel Oubelaid and Saif khameis Almazrouei (2023), Staying Ahead of Threats: A Review of AI and Cyber Security in Power Generation and Distribution. IJEER 11(1), 143-147. DOI: 10.37391/IJEER.110120.