GANSAT: A GAN and SATellite Constellation Fingerprint-Based Framework for GPS Spoof-Detection and Location Estimation in GPS Deprived Environment

This paper presents a robust system for mitigating adversarial and natural GPS disruptions by presenting: (1) a software-based defense mechanism against spoofing attacks using generative adversarial networks (GANs), The system detects unauthorized or spoofed GPS signals from a hardware based spoofer, and (2) deep neural network models to infer positioning information in GPS-degraded /denied environments using the novel idea of GPS satellite constellation fingerprint. As the GAN and Satellite constellation fingerprinting are used together in a unified framework, we call it the "GANSAT positioning system." Intuitively, the GANSAT neural networks implicitly learn a representation of the aggregation of the hardware fingerprints of the satellite's in the GPS constellation at a given location and time. To demonstrate the approach, raw GPS signals were collected from the satellite transmitters using a software defined radio (SDR) at five different locations in the Florida panhandle area of the United States. Additionally, a GPS spoofer is implemented using a SDR and an open source software and used in an uncontrolled laboratory environment for spoofing the GPS signals at the aforementioned locations. In our experiments, the GANSAT framework yields similar to 99.5% accuracy for the task of identifying and filtering the spoofed GPS signals from real ones. It also achieves similar to 100% accuracy for the task of location estimation.