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Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems

Author

Listed:
  • Fazliddin Makhmudov

    (Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea)

  • Dusmurod Kilichev

    (Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea)

  • Ulugbek Giyosov

    (Department of Exact Sciences, Kimyo International University in Tashkent, Tashkent 100121, Uzbekistan)

  • Farkhod Akhmedov

    (Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea)

Abstract

Electric vehicle (EV) charging systems are now integral to smart grids, increasing the need for robust and scalable cyberattack detection. This study presents an online intrusion detection system that leverages an Adaptive Random Forest classifier with Adaptive Windowing drift detection to identify real-time and evolving threats in EV charging infrastructures. The system is evaluated using real-world network traffic from the CICEVSE2024 dataset, ensuring practical applicability. For binary intrusion detection, the model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, and an F1-score of 0.9956, demonstrating highly accurate threat detection. It effectively manages concept drift, maintaining an average accuracy of 0.99 during drift events. In multiclass detection, the system attains 0.9840 accuracy, precision, and recall, with an F1-score of 0.9831 and an average drift event accuracy of 0.96. The system is computationally efficient, processing each instance in just 0.0037 s, making it well-suited for real-time deployment. These results confirm that online machine learning methods can effectively secure EV charging infrastructures. The source code is publicly available on GitHub, ensuring reproducibility and fostering further research. This study provides a scalable and efficient cybersecurity solution for protecting EV charging networks from evolving threats.

Suggested Citation

  • Fazliddin Makhmudov & Dusmurod Kilichev & Ulugbek Giyosov & Farkhod Akhmedov, 2025. "Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems," Mathematics, MDPI, vol. 13(5), pages 1-28, February.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:5:p:712-:d:1597386
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    References listed on IDEAS

    as
    1. Dusmurod Kilichev & Dilmurod Turimov & Wooseong Kim, 2024. "Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models," Mathematics, MDPI, vol. 12(4), pages 1-26, February.
    2. Jóni B. Santos & André M. B. Francisco & Cristiano Cabrita & Jânio Monteiro & André Pacheco & Pedro J. S. Cardoso, 2024. "Development and Implementation of a Smart Charging System for Electric Vehicles Based on the ISO 15118 Standard," Energies, MDPI, vol. 17(12), pages 1-25, June.
    3. Hoguk Lee & Minho Shin, 2024. "TestShark: A Passive Conformance Testing System for ISO 15118 Using Wireshark," Energies, MDPI, vol. 17(23), pages 1-15, November.
    4. Peng Zhang & Zifan Ma & Zeyuan Ren & Hongxiang Wang & Chuankai Zhang & Qing Wan & Dongxue Sun, 2024. "Design of an Automatic Classification System for Educational Reform Documents Based on Naive Bayes Algorithm," Mathematics, MDPI, vol. 12(8), pages 1-21, April.
    5. Sylvain Guillemin & Romain Choulet & Gregory Guyot & Sothun Hing, 2024. "Electrical Vehicle Smart Charging Using the Open Charge Point Interface (OCPI) Protocol," Energies, MDPI, vol. 17(12), pages 1-15, June.
    6. Mohammad Aldossary & Hatem A. Alharbi & Nasir Ayub, 2024. "Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability," Mathematics, MDPI, vol. 12(17), pages 1-29, August.
    7. Hari Mohan Rai & Joon Yoo & Saurabh Agarwal, 2024. "The Improved Network Intrusion Detection Techniques Using the Feature Engineering Approach with Boosting Classifiers," Mathematics, MDPI, vol. 12(24), pages 1-35, December.
    8. Fazila Malik & Qazi Waqas Khan & Atif Rizwan & Rana Alnashwan & Ghada Atteia, 2024. "A Machine Learning-Based Framework with Enhanced Feature Selection and Resampling for Improved Intrusion Detection," Mathematics, MDPI, vol. 12(12), pages 1-25, June.
    9. Xianer Ying & Mengshuang Pan & Xiner Chen & Yiyi Zhou & Jianhua Liu & Dazhi Li & Binghao Guo & Zihao Zhu, 2024. "Research on Virus Propagation Network Intrusion Detection Based on Graph Neural Network," Mathematics, MDPI, vol. 12(10), pages 1-11, May.
    10. Adel Binbusayyis, 2024. "Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers," Mathematics, MDPI, vol. 12(11), pages 1-25, May.
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