Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems DOI Creative Commons

Fazliddin Makhmudov,

Dusmurod Kilichev, Ulugbek Giyosov

и другие.

Mathematics, Год журнала: 2025, Номер 13(5), С. 712 - 712

Опубликована: Фев. 22, 2025

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 Adaptive Random Forest classifier with Windowing drift identify real-time evolving threats in EV infrastructures. The is evaluated using real-world network traffic from CICEVSE2024 dataset, ensuring practical applicability. For binary detection, model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, F1-score of 0.9956, demonstrating highly accurate threat It effectively manages concept drift, maintaining average accuracy 0.99 during events. In multiclass attains 0.9840 0.9831 event 0.96. computationally efficient, processing each instance just 0.0037 s, making it well-suited deployment. These results confirm machine learning methods can secure source code publicly available on GitHub, reproducibility fostering further research. provides a efficient cybersecurity solution protecting networks threats.

Язык: Английский

Online Machine Learning for Intrusion Detection in Electric Vehicle Charging Systems DOI Creative Commons

Fazliddin Makhmudov,

Dusmurod Kilichev, Ulugbek Giyosov

и другие.

Mathematics, Год журнала: 2025, Номер 13(5), С. 712 - 712

Опубликована: Фев. 22, 2025

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 Adaptive Random Forest classifier with Windowing drift identify real-time evolving threats in EV infrastructures. The is evaluated using real-world network traffic from CICEVSE2024 dataset, ensuring practical applicability. For binary detection, model achieves 0.9913 accuracy, 0.9999 precision, 0.9914 recall, F1-score of 0.9956, demonstrating highly accurate threat It effectively manages concept drift, maintaining average accuracy 0.99 during events. In multiclass attains 0.9840 0.9831 event 0.96. computationally efficient, processing each instance just 0.0037 s, making it well-suited deployment. These results confirm machine learning methods can secure source code publicly available on GitHub, reproducibility fostering further research. provides a efficient cybersecurity solution protecting networks threats.

Язык: Английский

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