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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

Shitharth Selvarajan1,2,3,*, Hariprasath Manoharan4, Taher Al-Shehari5, Hussain Alsalman6, Taha Alfakih7

1 School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS13HE, UK
2 Department of Computer Science and Engineering, Chennai Institute of Technology, Chennai, 602109, India
3 Centre for Research Impact & Outcome, Chitkara University, Chandigarh, 140401, India
4 Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, 600123, India
5 Department of Self-Development Skill, Common First Year Deanship, King Saud University, Riyadh, 11362, Saudi Arabia
6 Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
7 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

* Corresponding Author: Shitharth Selvarajan. Email: email

(This article belongs to the Special Issue: Best Practices for Smart Grid SCADA Security Systems Using Artificial Intelligence (AI) Models)

Computers, Materials & Continua 2025, 82(3), 4339-4369. https://doi.org/10.32604/cmc.2025.056100

Abstract

This research presents an analysis of smart grid units to enhance connected units’ security during data transmissions. The major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring, data expansion, control association, throughput, and losses. In addition, all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid networks. Moreover, the quantitative analysis of the optimization algorithm is discussed concerning two case studies, thereby achieving early convergence at reduced complexities. The suggested method ensures that each communication unit has its own distinct channels, maximizing the possibility of accurate measurements. This results in the provision of only the original data values, hence enhancing security. Both power and line values are individually observed to establish control in smart grid-connected channels, even in the presence of adaptive settings. A comparison analysis is conducted to showcase the results, using simulation studies involving four scenarios and two case studies. The proposed method exhibits reduced complexity, resulting in a throughput gain of over 90%.

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Cite This Article

APA Style
Selvarajan, S., Manoharan, H., Al-Shehari, T., Alsalman, H., Alfakih, T. (2025). Smart grid security framework for data transmissions with adaptive practices using machine learning algorithm. Computers, Materials & Continua, 82(3), 4339–4369. https://doi.org/10.32604/cmc.2025.056100
Vancouver Style
Selvarajan S, Manoharan H, Al-Shehari T, Alsalman H, Alfakih T. Smart grid security framework for data transmissions with adaptive practices using machine learning algorithm. Comput Mater Contin. 2025;82(3):4339–4369. https://doi.org/10.32604/cmc.2025.056100
IEEE Style
S. Selvarajan, H. Manoharan, T. Al-Shehari, H. Alsalman, and T. Alfakih, “Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm,” Comput. Mater. Contin., vol. 82, no. 3, pp. 4339–4369, 2025. https://doi.org/10.32604/cmc.2025.056100



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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