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Open Access

ARTICLE

Generative AI for Efficient and Secure Authentication in UAV-Enabled Smart City Transportation Systems

Akmalbek Abdusalomov1, Kudratjon Zohirov2, Sojida Ochilova2, Jakhongir Oramov3, Zafar Ruziyev3, Malika Rustamova4, Gulrukh Sherboboyeva5, Komil Tashev6,7, Young Im Cho1,*
1 Department of Computer Engineering, Gachon University Sujeong-Gu, Seongnam-si, Gyeonggi-Do, Republic of Korea
2 Department of Software and Technical/Hardware Support of Computer Systems, Karshi State Technical University, Karshi, Uzbekistan
3 Department of Finance and Banking, Karshi State Technical University, Karshi, Uzbekistan
4 Department of Optical Communication Systems and Networks, Karshi State Technical University, Karshi, Uzbekistan
5 Department of Information Systems and Technologies, Karshi State Technical University, Karshi, Uzbekistan
6 Department of Artificial Intelligence, Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi, Tashkent, Uzbekistan
7 Department of Information Processing and Management Systems, Tashkent State Technical University, Tashkent, Uzbekistan
* Corresponding Author: Young Im Cho. Email: email
(This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081292

Received 27 February 2026; Accepted 16 April 2026; Published online 07 May 2026

Abstract

Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted Risk-Adaptive Authentication (GRAA) system that modulates the intensity of the authentication process based on risk levels identified by mobility, contextual awareness, and the environment. The framework contains unlinkable pseudonymous credentials and, unlike the accumulator-based revocation scheme and AI-based trust evaluation, it is impossible to correlate sessions. The coherence with the majority of attacks is demonstrated under the formal analysis model, which is also based on the real-or-random (ROR) session key, alongside the justifications of forward secrecy and unlinkability. The performance analysis shows that GRAA can achieve up to 87.9% reduction in computation cost and 56.7% reduction in communication overhead compared to pairing-and-group signature schemes, while lowering the latency and energy consumption of the UAVs in a congested urban setting. Generally, the suggested architecture provides a scalable, convenient, and privacy-friendly authentication system for next-generation smart transportation systems that use UAVs.

Keywords

Generative AI; UAV-enabled intelligent transportation systems; risk-adaptive authentication; privacy-preserving security; unlinkability; scalable revocation

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