Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911032874631168 |
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| author | Dalamagkas, Christos Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos Papadopoulos, Ioannis Lagkas, Thomas Argyriou, Vasileios Goudos, Sotirios Margounakis, Dimitrios Fountoukidis, Eleftherios Sarigiannidis, Panagiotis |
| author_facet | Dalamagkas, Christos Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos Papadopoulos, Ioannis Lagkas, Thomas Argyriou, Vasileios Goudos, Sotirios Margounakis, Dimitrios Fountoukidis, Eleftherios Sarigiannidis, Panagiotis |
| contents | 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, like 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 the adoption of the FL architecture for the monitoring of the EV charging infrastructure and the detection of cyberattacks against the OCPP 1.6 protocol. The evaluation results showcase high detection performance of the proposed FL-based solution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01569 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks Dalamagkas, Christos Radoglou-Grammatikis, Panagiotis Bouzinis, Pavlos Papadopoulos, Ioannis Lagkas, Thomas Argyriou, Vasileios Goudos, Sotirios Margounakis, Dimitrios Fountoukidis, Eleftherios Sarigiannidis, Panagiotis Cryptography and Security 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, like 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 the adoption of the FL architecture for the monitoring of the EV charging infrastructure and the detection of cyberattacks against the OCPP 1.6 protocol. The evaluation results showcase high detection performance of the proposed FL-based solution. |
| title | Federated Detection of Open Charge Point Protocol 1.6 Cyberattacks |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2502.01569 |