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Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Author

Listed:
  • Ajit Kumar

    (School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea)

  • Neetesh Saxena

    (School of Computer Science and Informatics, Cardiff University, Cardiff CF10 3AT, UK)

  • Souhwan Jung

    (School of Electronic Engineering, Soongsil University, Seoul 06978, Korea)

  • Bong Jun Choi

    (School of Computer Science and Engineering, Soongsil University, Seoul 06978, Korea)

Abstract

Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2021:i:1:p:212-:d:713683
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    References listed on IDEAS

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    1. Rodofile, Nicholas R. & Radke, Kenneth & Foo, Ernest, 2019. "Extending the cyber-attack landscape for SCADA-based critical infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 14-35.
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    Cited by:

    1. 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.
    2. Muhammad Awais Shahid & Fiaz Ahmad & Rehan Nawaz & Saad Ullah Khan & Abdul Wadood & Hani Albalawi, 2023. "A Novel False Measurement Data Detection Mechanism for Smart Grids," Energies, MDPI, vol. 16(18), pages 1-17, September.

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