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A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning

Author

Listed:
  • Ines Ortega-Fernandez

    (Galician Research and Development Center in Advanced Telecommunications (GRADIANT), 36310 Vigo, Spain
    CITMAga, 15782 Santiago de Compostela, Spain
    Escola de Enxeñaría de Telecomunicación, Universidade de Vigo, 36310 Vigo, Spain)

  • Francesco Liberati

    (Department of Computer Control and Management Engineering (DIAG) “Antonio Ruberti”, University of Rome “La Sapienza”, Via Ariosto, 25, 00185 Rome, Italy)

Abstract

The smart grid merges cyber-physical systems (CPS) infrastructure with information and communication technologies (ICT) to ensure efficient power generation, smart energy distribution in real-time, and optimisation, and it is rapidly becoming the current standard for energy generation and distribution. However, the use of ICT has increased the attack surface against the electricity grid, which is vulnerable to a wider range of cyberattacks. In particular, Denial-of-Service (DoS) attacks might impact both the communication network and the cyber-physical layer. DoS attacks have become critical threats against the smart grid due to their ability to impact the normal operation of legitimate smart-grid devices and their ability to target different smart grid systems and applications. This paper presents a comprehensive and methodical discussion of DoS attacks in the smart grid, analysing the most common attack vectors and their effect on the smart grid. The paper also presents a survey of detection and mitigation techniques against DoS attacks in the smart grid using reinforcement learning (RL) algorithms, analysing the strengths and limitations of the current approaches and identifying the prospects for future research.

Suggested Citation

  • Ines Ortega-Fernandez & Francesco Liberati, 2023. "A Review of Denial of Service Attack and Mitigation in the Smart Grid Using Reinforcement Learning," Energies, MDPI, vol. 16(2), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:635-:d:1025640
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    References listed on IDEAS

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    1. Kyung Choi & Xinyi Chen & Shi Li & Mihui Kim & Kijoon Chae & JungChan Na, 2012. "Intrusion Detection of NSM Based DoS Attacks Using Data Mining in Smart Grid," Energies, MDPI, vol. 5(10), pages 1-19, October.
    2. Arman Goudarzi & Farzad Ghayoor & Muhammad Waseem & Shah Fahad & Issa Traore, 2022. "A Survey on IoT-Enabled Smart Grids: Emerging, Applications, Challenges, and Outlook," Energies, MDPI, vol. 15(19), pages 1-32, September.
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    Cited by:

    1. 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.
    2. Lorenzo Ricciardi Celsi & Anna Valli, 2023. "Applied Control and Artificial Intelligence for Energy Management: An Overview of Trends in EV Charging, Cyber-Physical Security and Predictive Maintenance," Energies, MDPI, vol. 16(12), pages 1-23, June.
    3. 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.

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