Security of Electric Vehicle Charging Infrastructure


   School of Computing, Engineering & the Built Environment

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  Dr Z Tan, Prof A Al-Dubai  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The rise of Electric Vehicles (EVs) drives an increase in demand and rapid development of the charging infrastructure, where a charger interconnects with an EV and a charging provider as well as a site controller to authorise charging sessions and report any errors. Charging providers may federate with each other to permit cross-network interoperation and will incorporate with energy companies for automated billing in the near future.

Exploring the operations and security of such an interconnected system is, therefore, crucial to avoid building insecure infrastructure. Attacks on these interconnected systems stretch their potential impact from relatively minor local effects to long-term national disruptions. This project aims to explore the Electric Vehicle Supply Equipment (EVSE) vulnerabilities and the impact of cyberattacks on the EVSE environment and to develop innovative protections (potentially based on zero-knowledge proof, physically unclonable function, attribute-based privacy-preserving data sharing and data-driven anomaly detection) to secure the EV charging infrastructure.

Academic qualifications

A first degree (at least a 2.1) ideally in Electronic Engineering or Computer Science with a good fundamental knowledge of Cybersecurity.

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components).

Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:

· Experience of fundamental System Security

· Competent in handling High Voltage Electrical Equipment and Critical Analysis

· Knowledge of Electric Vehicle Charing Infrastructure

· Good written and oral communication skills

· Strong motivation, with evidence of independent research skills relevant to the project

· Good time management

Desirable attributes:

· Experience in Electric Vehicle Supply Equipment Standards and Communication Protocols

· Good knowledge of Zero-knowledge Proof, Privacy-preserving Data Sharing

· Preliminary experience in Federated Learning and its applications

For enquiries about the content of the project, please email Dr Tan - [Email Address Removed]

For information about how to apply, please visit our website https://www.napier.ac.uk/research-and-innovation/research-degrees/how-to-apply

To apply, please select the link for the PhD Computing FT application form.

Computer Science (8) Engineering (12)

References

- Johnson, J., Anderson, B., Wright, B., Quiroz, J., Berg, T., Graves, R., ... & Hardy, K. (2022). Cybersecurity for Electric Vehicle Charging Infrastructure (No. SAND2022-9315). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
- Metere, R., Neaimeh, M., Morisset, C., Maple, C., Bellekens, X., & Czekster, R. M. (2021). Securing the electric vehicle charging infrastructure. arXiv preprint arXiv:2105.02905.
- Chamberlain, K., & Al-Majeed, S. (2021). Standardisation of UK Electric Vehicle Charging Protocol, Payment and Charge Point Connection. World Electric Vehicle Journal, 12(2), 63.
- Johnson, J., Berg, T., Anderson, B., & Wright, B. (2022). Review of Electric Vehicle Charger Cybersecurity Vulnerabilities, Potential Impacts, and Defenses. Energies, 15(11), 3931.
- Acharya, S., Dvorkin, Y., Pandžić, H., & Karri, R. (2020). Cybersecurity of smart electric vehicle charging: A power grid perspective. IEEE Access, 8, 214434-214453.
- Shamsoshoara, A., Korenda, A., Afghah, F., & Zeadally, S. (2020). A survey on physical unclonable function (PUF)-based security solutions for Internet of Things. Computer Networks, 183, 107593.
- Wang, D., Zhao, J., & Wang, Y. (2020). A survey on privacy protection of blockchain: the technology and application. IEEE Access, 8, 108766-108781.
- Nguyen, D. C., Ding, M., Pathirana, P. N., Seneviratne, A., Li, J., & Poor, H. V. (2021). Federated learning for internet of things: A comprehensive survey. IEEE Communications Surveys & Tutorials, 23(3), 1622-1658.
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 About the Project