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AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System

Author

Listed:
  • Aparna Kumari

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Rushil Kaushikkumar Patel

    (Computer Science and Engineering Department, R. N. G. Patel Institute of Technology, Surat 394620, Gujarat, India)

  • Urvi Chintukumar Sukharamwala

    (Computer Science and Engineering Department, R. N. G. Patel Institute of Technology, Surat 394620, Gujarat, India)

  • Sudeep Tanwar

    (Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India)

  • Maria Simona Raboaca

    (National Research and Development Institute for Cryogenic and Isotopic Technologies—ICSI Rm. Valcea, Uzinei Street, No. 4, P.O. Box 7 Raureni, 240050 Râmnicu Valcea, Romania)

  • Aldosary Saad

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

  • Amr Tolba

    (Computer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia)

Abstract

The existing grid infrastructure has already begun transforming into the next-generation cyber-physical smart grid (SG) system. This transformation has improved the grid’s reliability and efficiency but has exposed severe vulnerabilities due to growing cyberattacks and threats. For example, malicious actors may be able to tamper with system readings, parameters, and energy prices and penetrate to get direct access to the data. Several works exist to handle the aforementioned issues, but they have not been fully explored. Consequently, this paper proposes an AI-ADP scheme for the SG system, which is an artificial intelligence (AI)-based attack-detection and prevention (ADP) mechanism by using a cryptography-driven recommender system to ensure data security and integrity. The proposed AI-ADP scheme is divided into two phases: (i) attack detection and (ii) attack prevention. We employed the extreme gradient-boosting (XGBoost) mechanism for attack detection and classification. It is a new ensemble learning methodology that offers many advantages over similar methods, including built-in features, etc. Then, SHA-512 is used to secure the communication that employs faster performance, allowing the transmission of more data with the same security level. The performance of the proposed AI-ADP scheme is evaluated based on various parameters, such as attack-detection accuracy, cycles used per byte, and total cycles used. The proposed AI-ADP scheme outperformed the existing approaches and obtained 99.12% accuracy, which is relatively high compared to the pre-existing methods.

Suggested Citation

  • Aparna Kumari & Rushil Kaushikkumar Patel & Urvi Chintukumar Sukharamwala & Sudeep Tanwar & Maria Simona Raboaca & Aldosary Saad & Amr Tolba, 2022. "AI-Empowered Attack Detection and Prevention Scheme for Smart Grid System," Mathematics, MDPI, vol. 10(16), pages 1-18, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2852-:d:884988
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    References listed on IDEAS

    as
    1. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan K., 2021. "MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid," Applied Energy, Elsevier, vol. 285(C).
    2. Abdellah Chehri & Issouf Fofana & Xiaomin Yang, 2021. "Security Risk Modeling in Smart Grid Critical Infrastructures in the Era of Big Data and Artificial Intelligence," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
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