Tree-based Supervised Machine Learning Models For Detecting GPS Spoofing Attacks on UAS

Abstract

The security of Unmanned Aerial System (UAS) networks is becoming crucial as their number and application in several fields are increasing every day. For navigation and positioning, the Global Navigation System (GPS) is essential as it provides an accurate location for the UAS. However, since the civilian GPS signals are open and unencrypted, attackers target them in different ways such as spoofing attacks. To address this security concern, we propose a comparison of several tree-based machine learning models, namely Random Forest, Gradient Boost, XGBoost, and LightGBM, to detect GPS spoofing attacks. In this work, the dataset was built of real GPS signals that were collected using a Software Defined Radio unit and different types of simulated GPS spoofing attacks. The results show that XGBoost has the best accuracy (95.52%) and fastest detection time (2ms), which makes this model appropriate for UAS applications.