  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.
