DUONG, Cong Son (Universite Gustave Eiffel), Ercan, Secil (Université Gustave Eiffel), ZARGAYOUNA, Mahdi (French Institute for Sciences and Technology ofTransportation, D)

REM: A Multi-Model Fusion Framework for Real-Time Cyberattack Detection in Electric Vehicle Charging Systems

Scheduled for presentation during the Regular Session "S32a-AI-Driven Traffic Monitoring, Safety, and Anomaly Detection" (FR-LM-T32), Friday, November 21, 2025, 10:30−10:50, Southport 2

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

Abstract—The integration of Electric Vehicle Charging Systems tems (EVCS) into smart grids introduces new cybersecurity vulnerabilities due to insecure communication protocols. Threats such as denial-of-service attacks, malware injection, and spoofing compromise system reliability and safety. Current detection methods often underperform due to their reliance on single-source data and limited ability to generalize. We introduce the Resilient Electric Vehicle Charger for Cyberattack Classification Using Multi-Model Fusion (REM), a scalable and real-time framework for detecting cyberattacks. REM processes heterogeneous data sources—hardware performance counters and power consumption metrics—through three stages: (i) data preprocessing, (ii) independent model training for each data type, and (iii) probabilistic fusion of outputs to enhance prediction accuracy. Using the CICEVSE2024 dataset, REM achieves 98.57% accuracy in binary classification and 89.47% in multiclass tasks, with a response time of just 0.26 seconds. These results demonstrate REM’s potential as a high-performance, deployable solution for safeguarding EVCS against diverse cyber threats. The source code is publicly available at https://github.com/CongSon01/REM_ITCS.git.