Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems

Fazliddin Makhmudov, Dusmurod Kilichev, Ulugbek Giyosov and Farkhod Akhmedov ()
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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

Mathematics, 2025, vol. 13, issue 5, 1-28

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.

Keywords: Adaptive Random Forest; anomaly detection; concept drift; drift detection; Electric Vehicle Charging Systems; Internet of Things; intrusion detection; network security; online machine learning; real-time system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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