Performance Evaluation of Machine Learning-Based Cyber Attack Detection in Electric Vehicles Charging Stations

Electric Vehicles (EV) chargers rely on resource-constrained embedded hardware to execute critical charging operations. However, conventional security solutions may not adequately meet the needs of these devices. Increasingly, machine learning techniques are being leveraged to detect cyber attacks during electric vehicle charging. This study aims to evaluate various base machine learning methods and conduct binary and multi-class classification experiments to enhance security and operational efficiency in EV charging stations. The experiments utilize the CICEVSE2024 dataset, curated by the Canadian Institute for Cybersecurity at the University of New Brunswick, designed specifically for anomaly detection and establishing behavioral patterns in EV charging stations. The analysis highlights nuances in performance across different machine learning classifiers. For instance, Random Forest achieved 95.07% accuracy in binary classification by constructing robust decision trees. Ensemble methods such as CatBoost and LightGBM further improved binary classification to 95.37% and 95.41%, respectively through gradient boosting techniques. In multi-class attack classification, ensemble methods demonstrated superior performance, with the Stacking Ensemble achieving 91.1% accuracy by combining multiple models, and Voting Ensemble achieving 90.7%. Notably, among homogeneous base classifiers, Extra Trees and HistGradient Boosting were particularly effective, achieving 90.2% and 89.8% accuracy respectively in multi-class classification tasks. These findings underscore the efficacy of machine learning in enhancing cybersecurity measures for EV charging infrastructure.