A Real-time Machine Learning-based GPS Spoofing Solution for Location-dependent UAV Applications

In this paper, a three-class machine learning (ML) model is implemented on an unmanned aerial vehicle (UAV) with a Raspberry Pi processor for classifying two global positioning system (GPS) spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing a dataset collected in a previous work. This dataset conveys GPS-specific features, including location information. Models evaluations are carried out using the detection rate, F-score, false alarm rate, and misdetection rate, which all showed an acceptable performance. Then, the optimum model is loaded to the processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportations are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the developed model.