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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Open Access Research Article

A fuzzy mutual information-based intrusion detection system for enhancing cybersecurity in smart energy grids

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pp. 353–369Vol. 47Issue 1January 2026DOI: 10.47974/JIOS-2150XML
Received:
13 Aug 2025
Published Online:
25 Nov 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2150
Pages:
353–369

Abstract

The shift from traditional power grids to smart grids has enhanced efficiency, reliability, and flexibility through advanced digital communication, automation, and real-time control. However, this increased connectivity expands the attack surface, exposing smart grids to diverse cyber threats and legacy system vulnerabilities. Cybersecurity is, thus, essential to ensure reliable, safe, and secure energy services in smart grid scenarios. This paper proposes a novel FMI-Reduct-Based NIDS Classifier, which integrates a fuzzy approach along with Conditional Mutual Information (CMI) and the Quick Reduct Algorithm (QRA) to prioritize the most relevant features in smart grid data effectively. This methodology not only improves classification performance but also significantly boosts detection capabilities, providing a robust solution for addressing the challenges posed by evolving cyber threats in a smart grid. Extensive experiments using public and benchmark datasets such as NSL-KDD and UNSW-NB15 demonstrate that the proposed FMI-Reduct-Based Classifier outperforms traditional classifiers in precision, recall, and overall detection rate. The limitations and future research works are discussed thoroughly.

Keywords

Subject Classifications

03B5268T0193C9568M25

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