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.
","abstract_html":"<p>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.</p>","abstract_has_math":false,"creators":["Ondus, Norah"],"institution":null,"degree_name":"Master of Science in Cybersecurity Engineering","degree_level":"Thesis - Open Access","degree_discipline":"Electrical Engineering and Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-10-01T07:00:00Z","date_published":"2021-10-01T07:00:00Z","updated_at":"2026-07-27T19:25:23Z","subjects":["RF fingerprint","Machine Learning","UAV","Alexnet","Computer Engineering","Electrical and Computer Engineering","Navigation, Guidance, Control and Dynamics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://commons.erau.edu/edt/631","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Ondus, Norah"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering and Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis - Open Access"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Cybersecurity Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["RF fingerprint","Machine Learning","UAV","Alexnet","Computer Engineering","Electrical and Computer Engineering","Navigation, Guidance, Control and Dynamics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://commons.erau.edu/edt/631"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
"]},{"key":"dc:title","label":"Title","values":["RF Fingerprinting Unmanned Aerial Vehicles"]}]}],"canonical_facts":{"dc:creator":["Ondus, Norah"],"dc:description.abstract":["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.
"],"dc:identifier":["https://commons.erau.edu/edt/631"],"dc:subject":["RF fingerprint","Machine Learning","UAV","Alexnet","Computer Engineering","Electrical and Computer Engineering","Navigation, Guidance, Control and Dynamics"],"dc:title":["RF Fingerprinting Unmanned Aerial Vehicles"],"thesis:degree_discipline":["Electrical Engineering and Computer Science"],"thesis:degree_level":["Thesis - Open Access"],"thesis:degree_name":["Master of Science in Cybersecurity Engineering"]},"updated_at":"2026-07-27T19:25:23Z"}