Dhanda Supriya, Anne Venkata Praveen Krishna | International Journal of Electrical Power System and Technology | Vol 12, Issue 02
Abstract
In the realm of cybersecurity, the intersection of technology and security measures has spurred significant advancements, particularly in safeguarding electrical systems against cyber threats. This research delves into the evolving landscape of cybersecurity within electrical systems, leveraging machine learning techniques to mitigate challenges. By incorporating cybersecurity protocols with machine learning algorithms, this study demonstrates enhanced security measures tailored for electrical infrastructures, pivotal in ensuring efficiency and resilience. Through meticulous examination, various machine learning models were scrutinized for their efficacy in fortifying security, utilizing diverse datasets pertinent to system vulnerabilities. The findings reveal compelling insights, showcasing the effectiveness of machine learning algorithms in bolstering security measures. Notably, the comparative analysis highlights the prowess of Random Forest with an accuracy of 99.89% in detecting a myriad of attacks with exceptional accuracy, outperforming its counterparts such as Decision Tree and KNN. Additionally, it's observed that while Random Forest achieved superior accuracy, Decision Tree and KNN struggled to recognize specific attack types like multihop, snmpgetattack, and spy attacks. This research underscores the imperative synergy between cybersecurity frameworks and machine learning methodologies, illuminating pathways for fortifying smart electrical systems against cyber threats while ensuring reliable, scalable, adaptive, and real- time protection against evolving cyberattack scenarios and vulnerabilities.
Keywords - Machine Learning, Cyber Security, Electrical Systems, Random Forest, KNN, Decision Tree, Cyber threats.
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How to cite this article
@article{SupriyaD2026,
author = {Dhanda Supriya and Anne Venkata Praveen Krishna},
title = {Securing Smart Electrical Systems through Comparative Analysis of Machine Learning Models for Classification},
journal = {International Journal of Electrical Power System and Technology},
year = {2026},
volume = {12},
number = {02},
url = {https://journalspub.com/publication/ijepst/article=26884}
}