<p>Across the electric vehicle life cycle, ensuring the security of electric vehicle supply equipment (EVSE) is of rising importance as cyber threats against charging infrastructure continue to escalate. This study presents a comprehensive architecture for securing and ensuring privacy in EVSE systems, with a focus on the CIC-EVSE-2024 dataset. The proposed methodology comprises four main stages: data preprocessing, centralized learning, federated learning, and blockchain-enhanced federated learning. Principal Component Analysis (PCA) and the Synthetic Minority Over-sampling Technique (SMOTE) were used to improve the quality of the data during preprocessing. During the centralized learning phase, various machine learning models, including the hybrid CNN-LSTM architecture, were trained and evaluated to determine their suitability for anomaly detection. The federated learning setup was therefore constructed in such a way as to ensure data privacy while granting decentralized model training across clients with non-independent and identically distributed (non-IID) data. Blockchain integration provides a secure platform for recording model updates, ensuring integrity and accountability. The experimental results demonstrated a notable improvement in the classification performance, thus proving that integrating advanced machine learning technologies with blockchain is a viable approach for addressing cybersecurity challenges in EVSE systems. This framework provides a scalable and privacy-aware solution for future EVSE infrastructure.</p>