Real-Time Intrusion Detection in Smart EV Charging Networks Using Embedded Deep Learning

The increasing connectivity of Electric Vehicle Supply Equipment (EVSE) within smart grid networks has compounded the threat of cyber exposures, particularly Distributed Denial-of-Service (DoS) attacks. This paper introduces a lightweight, real-time intrusion detection system based on a hybrid deep learning structure that intermixes Transformer encoders and a Multilayer Perceptron (MLP) classifier. The proposed model uses kernel-level event logs to capture both temporal dependencies and high-dimensional feature interactions. A well-structured preprocessing pipeline includes feature leakage prevention, normalization, class balancing, and stratified cross-validation. This approach ensures data integrity and effective learning. Experimental evaluation on a real-world dataset confirms the model’s superior performance with 100% accuracy, precision, recall, and F1-score, and ideal ROC-AUC and PR-AUC scores. Furthermore, the framework is streamlined for deployment on resource-constrained edge devices to enable decentralized, on-device threat detection. These results accentuate the effectiveness and viability of the suggested solution in enhancing the cybersecurity posture of modern EV charging ecosystems