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

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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