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The emerging, practical and observed issue of how to detect rogue drones carrying terrestrial user equipment (UE) 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. Simulation results show that the proposed machine learning solutions can achieve high rogue drone detection rate for high altitudes while not mis-classifying regular ground based UEs as rogue drone UEs.