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.
Authors
- Vasileios Argyriou (ORCID: https://orcid.org/0000-0003-4679-8049)
- Dimitrios Margounakis (ORCID: https://orcid.org/0000-0002-7598-0934)
- Panagiotis Radoglou‐Grammatikis (ORCID: https://orcid.org/0000-0003-1605-9413)
- Christos Dalamagkas (ORCID: https://orcid.org/0000-0002-0210-5290)
- Sotirios K. Goudos (ORCID: https://orcid.org/0000-0001-5981-5683)
- Pavlos S. Bouzinis (ORCID: https://orcid.org/0000-0003-4861-475X)
- Eleftherios Fountoukidis
- Panagiotis Sarigiannidis (ORCID: https://orcid.org/0000-0001-6042-0355)
- Θωμάς Λάγκας (ORCID: https://orcid.org/0000-0002-0749-9794)
- Ioannis Papadopoulos
Institutions
- University of Western Macedonia (GR)
Publication Details
- Journal
- Complex Engineering Systems
- Published
- 2025-06-27
- DOI
- https://doi.org/10.20517/ces.2025.04
- Citations
- 4
- Primary Topic
- Blockchain Technology Applications and Security
- Type
- article
- Field-Weighted Citation Impact
- 12.98
Funders
- European Commission
- HORIZON EUROPE Framework Programme