Real-Time Intrusion Detection in Smart EV Charging Networks Using Embedded Deep Learning
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Abstract
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.
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Publication Details
- DOI: N/A
- Type of Publication:
- Conference Name: 2025 IEEE International Conference on Power, Electrical, Electronics and Industrial Applications (PEEIACON)
- Date of Conference: 04/12/2025 - 04/12/2025
- Venue: Southeast University (SEU), Dhaka, Bangladesh
- Organizer: IEEE Bangladesh section (IEEE BDS) and IEEE Industry Applications Society (IAS) Bangladesh