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

Abstract

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 ~99.5% accuracy for the task of identifying and filtering the spoofed GPS signals from real ones. It also achieves ~100% accuracy for the task of location estimation.

Keywords

• GPS
• GNSS
• positioning
• machine learning
• generative adversarial nets
• deep neural network