Open Access

ARTICLE

Context-Aware Identity Validation for UAV-Assisted Urban Mobility and Traffic Monitoring Environments

Kuldashbay Avazov¹, Kudratjon Zohirov², Alpamis Kutlimuratov³, Charos Khidirova⁴, Jasur Sevinov^5,6, Urishev Omadjon⁷, Adilbek Dauletov⁸, Akmalbek Abdusalomov^4,5,9, Young Im Cho^1,*

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 Applied Informatics, Kimyo International University in Tashkent, Uzbekistan
4 Department of Computer Systems and Artificial Intelligence, Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi, Tashkent, Uzbekistan
5 Department of Information Processing and Control Systems, Tashkent State Technical University, Tashkent, Uzbekistan
6 Department of Computer Engineering, University of Tashkent for Applied Sciences, Tashkent, Uzbekistan
7 Department of Electronics and Instrumentation, Fergana State Technical University, Fergana, Uzbekistan
8 Department of Digital Technologies, Alfraganus University, Yukori Karakamish Street 2a, Tashkent, Uzbekistan
9 Department of Artificial Intelligence, Tashkent State University of Economics, Tashkent, Uzbekistan

* Corresponding Author: Young Im Cho. Email:

(This article belongs to the Special Issue: Integrating Generative AI with UAVs for Autonomous Navigation and Decision Making)

Computers, Materials & Continua 2026, 88(3), 69 https://doi.org/10.32604/cmc.2026.083828

Received 11 April 2026; Accepted 26 May 2026; Issue published 23 July 2026

Abstract

Unmanned aerial vehicles (UAVs) are becoming a common solution to urban mobility, and traffic monitoring as well, owing to their ability to be deployed flexibly, ability to see a broader area and real-time sensing. However, the reliability of UAV-assisted traffic systems can be compromised through identity spoofing, Sybil attacks, false data injection, and trajectory manipulation. Current authentication techniques primarily verify cryptographic identities but often cannot detect when a claimed identity is inconsistent with physical movement patterns and settings. To overcome this drawback, this paper presents a context-aware identity validation system, CIV-UAV, for UAV-based urban traffic surveillance. The paradigm combines a model of cryptographic validation, model mobility, on-the-fly visual, road-network, temporal continuity, anomaly scoring, and multi-UAV consensus into a cohesive trust-based validation model. The risk-adaptive policy also adjusts the validation strictness based on the seriousness of the situation and the level of uncertainty. The outcomes of simulations indicate that CIV-UAV enhances identity validation, lowers the false detection and false acceptance rates, and reinforces the detection of spoofing, Sybil behaviour, path forgery, injection of fake events, and vision-communication mismatch attacks. The suggested architecture provides an identity validation system that is easy to implement and can be upgraded to a next-generation UAV-intelligent transportation network.

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Keywords

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