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Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods

Author

Listed:
  • Tehseen Mazhar

    (Department of Computer Science, Virtual University of Pakistan, Lahore 51000, Pakistan)

  • Hafiz Muhammad Irfan

    (Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan)

  • Sunawar Khan

    (Department of Computer Science, Islamia University Bahawalpur, Bahawalnagar 62300, Pakistan)

  • Inayatul Haq

    (School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Inam Ullah

    (BK21 Chungbuk Information Technology Education and Research Center, Chungbuk National University, Cheongju 28644, Republic of Korea)

  • Muhammad Iqbal

    (Institute of Computing and Information Technology, Gomal University, Dera Ismail Khan 29220, Pakistan)

  • Habib Hamam

    (Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada
    Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia
    International Institute of Technology and Management, Commune d’Akanda, Libreville 1989, Gabon
    Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

Smart grids are rapidly replacing conventional networks on a worldwide scale. A smart grid has drawbacks, just like any other novel technology. A smart grid cyberattack is one of the most challenging things to stop. The biggest problem is caused by millions of sensors constantly sending and receiving data packets over the network. Cyberattacks can compromise the smart grid’s dependability, availability, and privacy. Users, the communication network of smart devices and sensors, and network administrators are the three layers of an innovative grid network vulnerable to cyberattacks. In this study, we look at the many risks and flaws that can affect the safety of critical, innovative grid network components. Then, to protect against these dangers, we offer security solutions using different methods. We also provide recommendations for reducing the chance that these three categories of cyberattacks may occur.

Suggested Citation

  • Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:2:p:83-:d:1073360
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    References listed on IDEAS

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    Cited by:

    1. Ameni Boumaiza, 2024. "A Blockchain-Based Scalability Solution with Microgrids Peer-to-Peer Trade," Energies, MDPI, vol. 17(4), pages 1-18, February.
    2. Grigorii Asyaev & Alexander Sokolov & Alexey Ruchay, 2023. "Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    3. Mousa Mohammed Khubrani & Shadab Alam, 2023. "Blockchain-Based Microgrid for Safe and Reliable Power Generation and Distribution: A Case Study of Saudi Arabia," Energies, MDPI, vol. 16(16), pages 1-34, August.
    4. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
    5. Guixiang Cao & Xintong Fang & Ying Chen & Jinghuai She, 2023. "Regional Big Data Application Capability and Firm Green Technology Innovation," Sustainability, MDPI, vol. 15(17), pages 1-29, August.

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