Unmanned Aerial Vehicle (UAV) swarms’ dependence on BeiDou Global Navigation Satellite System (GNSS) signals exposes them to high-risk spoofing attacks that can severely compromise spatial coordination, navigation fidelity, and mission continuity. To address these vulnerabilities, this study proposes a novel, multi-layered detection and mitigation framework tailored for decentralized UAV networks to enable real-time spoofing resilience. The architecture integrates a residual-based anomaly tracker using a Kalman filter, supervised signal classification through ensemble models (XGBoost and Random Forest), and a transformer-based model that leverages temporal UAV telemetry to detect contextual irregularities associated with spoofing activity. A purpose-built simulation platform replicating complex urban threat environments, which includes multipath interference and adversarial UAV-based spoofers, was used to rigorously assess system performance under realistic attack scenarios. Experimental evaluations reveal that the hybrid framework consistently achieves detection accuracies close to 99%, maintains false alarm rates below 2%, and initiates mitigation responses within an average of 3 s. The system preserves swarm coordination and navigational precision by sustaining mission success rates above 97% under active spoofing. These results demonstrate the efficacy of integrating statistical estimation, machine learning inference, and temporal-context modeling for robust GNSS spoofing defense. The proposed solution advances the security of UAV swarms and paves the way for practical deployment by offering efficient, low-latency inference and adaptable control strategies in adversarial operational environments.
Tariq et al. (Tue,) studied this question.