Federated detection of open charge point protocol 1.6 cyberattacks
Christos Dalamagkas , Panagiotis Radoglou-Grammatikis , Pavlos Bouzinis , Ioannis Papadopoulos , Thomas Lagkas , Vasileios Argyriou , Sotirios Goudos , Dimitrios Margounakis , Eleftherios Fountoukidis , Panagiotis Sarigiannidis
Complex Engineering Systems ›› 2025, Vol. 5 ›› Issue (2) : 9
Federated detection of open charge point protocol 1.6 cyberattacks
The ongoing electrification of the transportation sector requires the deployment of multiple Electric Vehicle (EV) charging stations across multiple locations. However, the EV charging stations introduce significant cyber-physical and privacy risks, given the presence of vulnerable communication protocols, such as the Open Charge Point Protocol (OCPP). Meanwhile, the Federated Learning (FL) paradigm showcases a novel approach for improved intrusion detection results that utilize multiple sources of Internet of Things data, while respecting the confidentiality of private information. This paper proposes an FL-based intrusion detection system, which leverages OCPP 1.6 network flows to detect OCPP 1.6 cyberattacks. The evaluation results showcase high detection performance of the proposed FL-based solution.
Anomaly detection / cybersecurity / Open Charge Point Protocol 1.6 / Federated Learning
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