  The emergence of drones has added new dimension to privacy and security
issues. There are little or no strict regulations on the people that can
purchase or own a drone. For this reason, people can take advantage of these
aircraft to intrude into restricted or private areas. A Drone Detection and
Identification (DDI) system is one of the ways of detecting and identifying the
presence of a drone in an area. DDI systems can employ different sensing
technique such radio frequency (RF) signals, video, sounds and thermal for
detecting an intruding drone. In this work, we propose a machine learning
RF-based DDI system that uses low band RF signals from drone-to-flight
controller communication. We develop three machine learning models using the
XGBoost algorithm to detect and identify the presence of a drone, the type of
drones and the operational mode of drones. For these three XGBoost models, we
evaluated the models using 10-fold cross validation and we achieve average
accuracy of 99.96%, 90.73% and 70.09% respectively.
