Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Acceso en línea: | |
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| _version_ | 1866914594230894592 |
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| author | Bean, Joshua Manias, Dimitrios Michael |
| author_facet | Bean, Joshua Manias, Dimitrios Michael |
| contents | Electric Vehicles (EVs) have emerged as significant disruptors in the transportation sector over the past decade. Their growing popularity and adoption are accompanied by capital expenditures to deploy charging infrastructure. EV charging infrastructure sits at the intersection of the power grid, the network, and the vehicular client, creating an attractive surface for cyberattacks. Many machine learning-based cybersecurity countermeasures have been developed using various public and private datasets. These countermeasures, often intrusion detection systems, are limited in performance by the quality and expressivity of the training data. This work explores the most common datasets and modeling methods, identifies key limitations and open challenges, and proposes future directions to continue catalyzing innovation in the field. By addressing these data limitations, intrusion detection systems are better positioned to address the constantly evolving cyberthreat landscape of EV charging infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_24190 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions Bean, Joshua Manias, Dimitrios Michael Cryptography and Security Electric Vehicles (EVs) have emerged as significant disruptors in the transportation sector over the past decade. Their growing popularity and adoption are accompanied by capital expenditures to deploy charging infrastructure. EV charging infrastructure sits at the intersection of the power grid, the network, and the vehicular client, creating an attractive surface for cyberattacks. Many machine learning-based cybersecurity countermeasures have been developed using various public and private datasets. These countermeasures, often intrusion detection systems, are limited in performance by the quality and expressivity of the training data. This work explores the most common datasets and modeling methods, identifies key limitations and open challenges, and proposes future directions to continue catalyzing innovation in the field. By addressing these data limitations, intrusion detection systems are better positioned to address the constantly evolving cyberthreat landscape of EV charging infrastructure. |
| title | Cybersecurity of Electric Vehicle Charging Infrastructure: Recent Advances, Open Challenges, and Future Directions |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.24190 |