Machine Learning-Based GPS Spoofing Detection and Mitigation for UAVs

The rapid proliferation of commercial unmanned aerial vehicles (UAVs) has revolutionized fields such as precision agriculture and disaster response. However, their heavy reliance on GPS navigation leaves them highly vulnerable to spoofing attacks, with potentially severe consequences. To mitigate this threat, we present a machine learning-driven framework for real-time GPS spoofing detection, designed with a balance of detection accuracy and computational efficiency. Our work is distinguished by the creation of a comprehensive dataset of 10,000 instances that integrates both simulated and real-world data, enabling robust and generalizable model development. A comprehensive evaluation of multiple classification algorithms identifies XGBoost as the superior performer, achieving 93.07% accuracy alongside outstanding precision, recall, and F1-scores. Beyond standard classification metrics, our assessment encompasses ROC-AUC, detection latency, and false positive rate, providing a comprehensive assessment of performance. This work contributes to UAV security by providing a robust and reproducible solution for detecting GPS spoofing attacks, supported by a detailed methodology, a comprehensive evaluation including inference-time latency, and a publicly available dataset.