Deep Learning for GPS Spoofing Detection in Cellular Enabled Unmanned Aerial Vehicle Systems
This addresses a security threat for UAV navigation in beyond visual line of sight operations, but it is incremental as it applies existing deep learning methods to a specific domain problem.
The paper tackles GPS spoofing detection in cellular-enabled UAV systems by proposing a deep learning-based approach using an MLP model trained on path loss statistics, achieving over 93% accuracy with three base stations and 80% with one.
Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position. Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.