Date of Award

Fall 2021

Access Type

Thesis - Open Access

Degree Name

Master of Science in Cybersecurity Engineering


Electrical Engineering and Computer Science

Committee Chair

Laxima Niure Kandel

First Committee Member

Houbing Song

Second Committee Member

Omar Ochoa


As unmanned aerial vehicles (UAVs) continue to become more readily available, their use in civil, military, and commercial applications is growing significantly. From aerial surveillance to search-and-rescue to package delivery the use cases of UAVs are accelerating. This accelerating popularity gives rise to numerous attack possibilities for example impersonation attacks in drone-based delivery, in a UAV swarm, etc. In order to ensure drone security, in this project we propose an authentication system based on RF fingerprinting. Specifically, we extract and use the device-specific hardware impairments embedded in the transmitted RF signal to separate the identity of each UAV. To achieve this goal, AlexNet with the data augmentation technique was employed.