FL-EVCS: Federated Learning based Anomaly Detection for EV Charging Ecosystem

The rapid expansion of electric vehicle (EV) technologies and their seamless integration into smart grids herald a significant transition toward sustainable energy practices, highlighting the need to bolster Electric Vehicle Charging Stations (EVCS). These stations, connected through protocols like ISO 15118 and OCPP, ensure secure interactions between EVs, EVCS, and management systems. However, the rise of Internet of Things (IoT) connectivity exposes EVCS to cyber threats, including Distributed Denial of Service (DDoS), Man-in-the-Middle (MitM), and Injection attacks, which threaten the stability of thousands of EVCS and the overall charging infrastructure. In response to these challenges, this paper presents a Federated Learning-based Anomaly Detection System (FL-EVCS), an approach that significantly enhances the cybersecurity framework within EVCS networks. By adopting a federated learning model, FL-EVCS prioritizes data privacy by exchanging model parameters instead of raw data across the network. This method offers a powerful collective defense strategy, considerably enhancing the capabilities of traditional ADS that utilize machine learning algorithms such as KNN, RF, and SVM. In our evaluations, FL-EVCS demonstrated good performance by achieving an accuracy of around 97% and superior F1-scores, compared to traditional ML-based ADS using the CICEVSE2024 dataset. By enhancing EVCS security against cyber threats and supporting EV market growth with a secure user-friendly infrastructure, FL-EVCS markedly increases detection accuracy and efficiency. This approach offers a well-balanced solution for anomaly detection, meeting stringent data privacy requirements and promoting the resilience and expansion of the EVCS ecosystem.