Cybersecurity Enhancement in Electric Vehicle Systems using Principal Component Analysis (PCA)
DOI:
https://doi.org/10.15662/IJRAI.2026.0903007Keywords:
EVSE, Cybersecurity, Intrusion Detection System, PCA, Dimensionality Reduction, Random Forest, SMOTE, CICEVSE2024, HMAC-SHA256Abstract
The increasing deployment of Electric Vehicle Supply Equipment (EVSE) introduces significant cybersecurity vulnerabilities in EV charging networks. Traditional Intrusion Detection System (IDS) is computationally intensive and cannot be used in resource-limited EVSE environments. The paper suggests a lightweight, PCA based, cybersecurity framework to detect and monitor real-time cyber-attack in EVSE systems. The dataset is CICEVSE2024 (464,165 samples, 74 features of two real EVSE charging stations) on which four dimensionality reduction strategies are tested: Baseline (74 features), Standard PCA (3 components), Randomized PCA (2 components), and Mini-Batch Sparse PCA (4 components). Three machine learning classifiers, namely, Random Forest, Bagging, and ANN (MLP) are also trained on the reduced features sets. The framework proposed has F1-scores of 87.7-92.1% with AUC-ROC values of 0.943-0.978, and inference time that is 3-5x lower than those of baseline models. Random Forest using standard PCA provides the best accuracy-efficiency trade-off (F1=89.9% inference 0.235 s). An implementation is the Flask-based Security Operations Centre (SOC) dashboard which supports HMAC-SHA256 authenticated multi-station support, automated email/SMS alerting, and GPS location-aware reporting, which is validated as a deployable prototype.References
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