CyberSentry: Enhancing SCADA security through advanced deep learning and optimization strategies
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Le résumé fourni par la source
SCADA systems form the core of infrastructural facilities, including power grids, water treatment facilities, and industrial processes. Changing cyber threats present increasingly sophisticated attacks against which traditional security models inadequately protect SCADA systems. These traditional models usually have drawbacks in the way of inadequate feature selection, inefficiency in detecting most attacks, and suboptimal parameter tuning, which cause vulnerabilities and reduce resilience in systems. This paper presents CyberSentry, a new security framework designed to overcome limitations so as to provide robust protection for SCADA systems. These three modules makeup CyberSentry: the RMIG feature selection model, tri-fusion net for attack detection, and Parrot-Levy Blend Optimization (PLBO) for parameter tuning. The Recursive Multi-Correlation-based Information Gain (RMIG) feature selection model enhances accuracy in detection by optimizing the set of fatal features through recursive multi-correlation analysis by Information Gain prioritization. The Tri-Fusion Net combines anomaly detection, signature-based detection, and machine learning classifiers to enhance the detection versatility and robustness. The PLBO module ensures efficient and dynamic tuning for the parameters through undocumented Parrot and Levy optimization techniques. The proposed CyberSentry framework integrates, within a unified architecture, anomaly detection, signature-based detection, and machine learning classifiers to enhance the security of SCADA systems against diverse cyber threats. Features extracted in this manner are analyzed using machine learning classifiers that exploit their predictive capabilities for robust threat classification. The proposed approaches are fused within the Tri-Fusion Net to complement each other in areas where the separate methods lack certain strengths. This, therefore, ensures broad threat detection, as is validated by extensive testing with various datasets for the assurance of superiority in accuracy and reliability. Validated and tested against a wide variety of datasets, CyberSentry demonstrates an overall accuracy of 99.5% and a loss of 0.32, proving that this method is both effective and reliable.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- CyberSentry: Enhancing SCADA security through advanced deep learning and optimization strategies
- Date Crossref
- 01/09/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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King Abdulaziz University pays non établi dans la noticeUniversité ou école supérieure
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Chitkara University Centre for Research Impact & Outcome pays non établi dans la noticeUniversité ou école supérieure
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Leeds Beckett University pays non établi dans la noticeUniversité ou école supérieure
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King Saud University Department of Self-Development Skill pays non établi dans la noticeUniversité ou école supérieure
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Lovely Professional University Division of research and development pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Computing and Information Technology Department of Information Technology pays non établi dans la noticeUniversité ou école supérieure
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Chennai Institute of Technology Department of Computer Science and Engineering pays non établi dans la noticeStructure de recherche
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School of Built Environment pays non établi dans la noticeUniversité ou école supérieure
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College of Computer and Information Sciences Computer Science Department pays non établi dans la noticeUniversité ou école supérieure
King Abdulaziz University, Centre for Research Impact & Outcome — Chitkara University et Leeds Beckett University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.