TY  - EJOU
AU  - Namagembe, Charlotte Olivia 
AU  - Ibrahim, Mohamad 
AU  - Rahman, Md Arafatur 
AU  - Pillai, Prashant 

TI  - Machine Learning-Based GPS Spoofing Detection and Mitigation for UAVs
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Commercial unmanned aerial vehicles; global positioning systems; machine learning techniques; spoofing attack

DO  - 10.32604/cmc.2025.070316