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Editor-in-Chief
Nikiforov
Vladimir O.
D.Sc., Prof.
Partners
doi: 10.17586/2226-1494-2025-25-4-755-761
An improved authentication protocol for self-driving vehicles based on Diffie–Hellman algorithm
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Article in English
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Abstract
For citation:
Saeed M.S., Bezzateev S.V. An improved authentication protocol for self-driving vehicles based on Diffie–Hellman algorithm. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2025, vol. 25, no. 4, pp. 755–761. doi: 10.17586/2226-1494-2025-25-4-755-761
Abstract
Authentication is a critical challenge in autonomous vehicles, particularly within Controller Area Networks which are prone to various cyber threats. Existing protocols often fall short in balancing strong security guarantees with computational efficiency and privacy preservation. In this paper, we propose a lightweight authentication protocol based on the Decisional Diffie–Hellman problem, specifically designed for Controller Area Network environments. The protocol employs lightweight cryptographic operations to verify vehicle authenticity and validate data messages, while also maintaining anonymity by regularly updating login identities. It also supports password changes without requiring a trusted third party. The protocol security is formally verified using Burrows-Abadi-Needham logic. Performance evaluation shows that our approach significantly reduces computational overhead, achieving an execution time of 0.90908 ms, outperforming existing solutions in the literature. By combining formal verification with practical efficiency, the proposed protocol offers a robust solution for secure and efficient authentication in resource-constrained vehicular networks. Its lightweight design and anonymity-preserving mechanisms make it particularly suitable for real-time autonomous vehicle applications.
Keywords: autonomous vehicles, controller area networks, authentication protocol, efficient authentication, anonymity-preserving mechanisms
Acknowledgements. Работа выполнена в рамках государственного задания (проект № FSER-2025-0003).
References
Acknowledgements. Работа выполнена в рамках государственного задания (проект № FSER-2025-0003).
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