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Unmanned Aerial Systems (UAS) heavily depend on the Global Positioning System (GPS) for navigation. However, the unencrypted civilian GPS signals are subject to different types of threats, including GPS spoofing attacks. In this paper, we evaluate five instance-based learning models for GPS spoofing detection in UAS, namely K Nearest Neighbor, Radius Neighbor, Linear Support Vector Machine (SVM), C-SVM, and Nu-SVM. We used software-defined radio units to collect and extract features from satellite signals. Then, we simulated three types of GPS spoofing attacks specifically the simplistic, intermediate, and sophisticated attacks. The evaluation results show that Nu-SVM outperforms the other instance learning classifiers in terms of accuracy, probability of detection, probability of false alarm, and probability of misdetection. In addition, the model shows good computational performance regarding memory usage and processing time in the detection phase.