Detecting Stealthy GPS Spoofing Attack Against UAVs Using Onboard Sensors

Anthony Finn, Mengjie Jia, Yanyan Li, Jiawei Yuan

Abstract: Unmanned aerial vehicles (UAVs), known as drones, have gained significant popularity across various military, civilian, and commercial applications. Given the fact that many UAV operations rely on the Global Positioning System (GPS), they inevitably become susceptible to GPS spoofing attacks. In recent years, AI-enabled detection approaches toward UAV GPS spoofing attacks have increasingly received research attention. Therefore, it is crucial to have a systematical understanding of GPS spoofing attacks and collect a comprehensive and quality data set in the construction of effective AI-enabled detection. This paper aims to collect a large dataset of UAV flights under normal and attack scenarios and design an effective detection approach for stealthy UAV GPS spoofing attacks using onboard sensors and machine learning. 30 different features from 4 onboard UAV sensors are extracted in constructing effective AI models. On top of that, we examined different deep learning and machine learning models by fusing important features from our analysis. Our evaluation results in different flight scenarios demonstrated the effectiveness of our proposed approach, in which a high detection accuracy up to 98.7% and a fast detection time of 0.5 second can be achieved using the XGBoost model.