  The emerging, practical and observed issue of how to detect rogue drones that
carry terrestrial user equipment (UEs) on mobile networks is addressed in this
paper. This issue has drawn much attention since the rogue drones may generate
excessive interference to mobile networks and may not be allowed by regulations
in some regions. In this paper, we propose a novel machine learning approach to
identify the rogue drones in mobile networks based on radio measurements. We
apply two classification machine learning models, Logistic Regression, and
Decision Tree, using features from radio measurements to identify the rogue
drones. We find that for high altitudes the proposed machine learning solutions
can yield high rogue drone detection rate while not mis-classifying regular
ground based UEs as rogue drone UEs. The detection accuracy however degrades at
low altitudes.

Networking and Internet Architecture