Publication: Drone Detection and Identification by Using Packet Length Signature
Issued Date
2018-09-06
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2-s2.0-85057755102
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018)
Suggested Citation
Pongjarun Kosolyudhthasarn, Vasaka Visoottiviseth, Doudou Fall, Shigeru Kashihara Drone Detection and Identification by Using Packet Length Signature. Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018. (2018). doi:10.1109/JCSSE.2018.8457352 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/45578
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Title
Drone Detection and Identification by Using Packet Length Signature
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Abstract
© 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.