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Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

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  • Nakkeeran Murugesan

    (Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • Anantha Narayanan Velu

    (Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • Bagavathi Sivakumar Palaniappan

    (Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • Balamurugan Sukumar

    (Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India)

  • Md. Jahangir Hossain

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia)

Abstract

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

Suggested Citation

  • Nakkeeran Murugesan & Anantha Narayanan Velu & Bagavathi Sivakumar Palaniappan & Balamurugan Sukumar & Md. Jahangir Hossain, 2024. "Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid," Energies, MDPI, vol. 17(8), pages 1-34, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1965-:d:1379573
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    References listed on IDEAS

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    1. Derya Betul Unsal & Taha Selim Ustun & S. M. Suhail Hussain & Ahmet Onen, 2021. "Enhancing Cybersecurity in Smart Grids: False Data Injection and Its Mitigation," Energies, MDPI, vol. 14(9), pages 1-36, May.
    2. Kimani, Kenneth & Oduol, Vitalice & Langat, Kibet, 2019. "Cyber security challenges for IoT-based smart grid networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 36-49.
    3. Ajit Kumar & Neetesh Saxena & Souhwan Jung & Bong Jun Choi, 2021. "Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling," Energies, MDPI, vol. 15(1), pages 1-22, December.
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