Abstract
The invention developed by the University of Central Florida
and the University of Alabama is a robust, deep neural network model, named
GANSAT, which is a defense mechanism against adversarial attacks that can degrade
the performance of Global Positioning System (GPS) applications. This model is
based on the framework of generative adversarial networks (GANs) to detect
unauthorized or spoofed GPS signals. The presented model may predict positions
in a GPS-degraded/denied environment using the novel concept of GPS satellite
constellation fingerprint; the neural network implicitly learns the individual satellite’s
relative position to each other and to the receiver, based on its contribution
to the received GPS signal. The proposed GANSAT framework yields approximately 99.5% accuracy for identifying and filtering out
the spoofed GPS signals from real ones.
Benefit
GANSAT disambiguates spoofed GPS signals from real onesCompatible with any existing GPS systemMarket Application
CybersecurityMilitaryTransportationPublications
GANSAT: A GAN Based System for
Detecting GPS Spoofer using SATellite Constellation Fingerprinting, MobiCom ’20: ACM 26th Annual
International Conference on Mobile Computing and Networking, Sep 1–25, 2020, London, United Kingdom.
Brochure