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Deep Machine Learning Model-Based Cyber-Attacks Detection in Smart Power Systems

Author

Listed:
  • Abdulaziz Almalaq

    (Department of Electrical Engineering, Engineering College, University of Ha’il, Ha’il 55476, Saudi Arabia)

  • Saleh Albadran

    (Department of Electrical Engineering, Engineering College, University of Ha’il, Ha’il 55476, Saudi Arabia)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt)

Abstract

In this study, a deep learning-based attack detection model is proposed to address the problem of system disturbances in energy systems caused by natural events like storms and tornadoes or human-made events such as cyber-attacks. The proposed model is trained using the long time recorded data through accurate phasor measurement units (PMUs). The data is then sent to various machine learning methods based on the effective features extracted out using advanced principal component analysis (PCA) model. The performance of the proposed model is examined and compared with some other benchmarks using various indices such as confusion matrix. The results show that incorporating PCA as the feature selection model could effectively decrease feature redundancy and learning time while minimizing data information loss. Furthermore, the proposed model investigates the potential of deep learning-based and Decision Tree (DT) classifiers to detect cyber-attacks for improving the security and efficiency of modern intelligent energy grids. By utilizing the big data recorded by PMUs and identifying relevant properties or characteristics using PCA, the proposed deep model can effectively detect attacks or disturbances in the system, allowing operators to take appropriate action and prevent any further damage.

Suggested Citation

  • Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2022. "Deep Machine Learning Model-Based Cyber-Attacks Detection in Smart Power Systems," Mathematics, MDPI, vol. 10(15), pages 1-16, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2574-:d:870735
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    References listed on IDEAS

    as
    1. Abdulaziz Almalaq & Saleh Albadran & Amer Alghadhban & Tao Jin & Mohamed A. Mohamed, 2022. "An Effective Hybrid-Energy Framework for Grid Vulnerability Alleviation under Cyber-Stealthy Intrusions," Mathematics, MDPI, vol. 10(14), pages 1-20, July.
    2. Khalid Alnowibet & Andres Annuk & Udaya Dampage & Mohamed A. Mohamed, 2021. "Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework," Sustainability, MDPI, vol. 13(21), pages 1-32, October.
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    Cited by:

    1. Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2023. "An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    2. Fahad Alsokhiry & Andres Annuk & Toivo Kabanen & Mohamed A. Mohamed, 2022. "A Malware Attack Enabled an Online Energy Strategy for Dynamic Wireless EVs within Transportation Systems," Mathematics, MDPI, vol. 10(24), pages 1-20, December.
    3. Jin-Li Hu & Nhi Ha Bao Bui, 2024. "The Future Design of Smart Energy Systems with Energy Flexumers: A Constructive Literature Review," Energies, MDPI, vol. 17(9), pages 1-32, April.
    4. Izzuddin Fathin Azhar & Lesnanto Multa Putranto & Roni Irnawan, 2022. "Development of PMU-Based Transient Stability Detection Methods Using CNN-LSTM Considering Time Series Data Measurement," Energies, MDPI, vol. 15(21), pages 1-20, November.

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