SMDEVC: Specification-Based Misbehavior Detection for Electric Vehicle Charging Stations

초록

Electric vehicle (EV) chargers are often deployed in public spaces, making them easily accessible targets for cyber attackers. Moreover, EV chargers are components of EV charging infrastructure in which IT and OT domains coexist; thus, if they are compromised and used as intrusion points, they may affect the entire EV charging infrastructure. To address this issue, this paper proposes SMDEVC, which applies a specification-based intrusion detection paradigm to electric vehicle supply equipment (EVSE) by combining behavior-rule specification with UPPAAL verification. The proposed system specifies the normal behavior rules of EV chargers and classifies behaviors that deviate from these rules as abnormal. In addition, the proposed system reduces false positives through compliance-degree feedback. Using the CICEVSE2024 dataset, we conducted experiments to compare the detection performance and resource efficiency of the proposed system with those of machine learning, deep learning, and hybrid models. The experimental results show that the proposed SMDEVC achieved an accuracy of 96.0% and an F1-score of 97.9% on the CICEVSE2024 network dataset. In addition, the proposed system reduced peak memory usage by up to 99.6% compared with the other methods. © 2026 by the authors.

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