Enhancing EV charging station security: A multi-stage approach
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Date
2024-03
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University of New Brunswick
Abstract
The deployment of Electric Vehicle (EV) charging stations is pivotal to the global shift towards eco-friendly transportation. Nevertheless, as these systems become increasingly integrated into everyday life, they also emerge as prime targets for cybersecurity attacks. The development of cybersecurity solutions encounters challenges due to the deployment methods of EV charging stations, limitations in hardware resources, and the unavailability of attack datasets. Addressing this, our research introduces the creation and publication of a comprehensive dataset, CICEVSE2024, which includes 36GB of benign and attack samples. Additionally, we propose a multistage anomaly detection framework for identifying host- and network-based attacks on EV Supply Equipment (EVSE). A rule-based model is utilized at the EVSE level for preliminary detection. Subsequently, the Charging Station Monitoring System (CSMS) level employs three anomaly detection models alongside an attack classifier. Our approach ensures operational independence, allowing effective attack detection even when EVSE operates in standalone mode.