Pongjarun KosolyudhthasarnVasaka VisoottivisethDoudou FallShigeru KashiharaNara Institute of Science and TechnologyMahidol University2019-08-232019-08-232018-09-06Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)2-s2.0-85057755102https://repository.li.mahidol.ac.th/handle/20.500.14594/45578© 2018 IEEE. Unmanned Aerial Vehicle (UAV) as known as Drone has been becoming very popular around the world. However, a consumer UAV can be controlled from a long distance to record a video of occupants without permission, which causes privacy issues. Existing drone detection systems are required specific hardware and specialists to operate and deploy which are expensive for personal use. In this paper, we propose a drone detection and identification system which utilizes inexpensive commercial off-the-shelf (COTS) hardware and does not requires specialist knowledge to deploy. Our technical approach is to passively listen to the wireless signal between drone and its controller to observe for packet transmission characteristics of each drone. We evaluate our prototype system with three types of drones, which are Spark, AR, and Dobby. Our experiment results illustrate the feasibility of using the data frame length to identify the type of flying drone within 20 seconds.Mahidol UniversityComputer ScienceConference PaperSCOPUS10.1109/JCSSE.2018.8457352