TY - GEN
T1 - Drone Detection and Identification by Using Packet Length Signature
AU - Kosolyudhthasarn, Pongjarun
AU - Visoottiviseth, Vasaka
AU - Fall, Doudou
AU - Kashihara, Shigeru
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/6
Y1 - 2018/9/6
N2 - 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.
AB - 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.
KW - Detection
KW - Drone
KW - Identification
KW - UAV
KW - Wi-Fi
UR - https://www.scopus.com/pages/publications/85057755102
U2 - 10.1109/JCSSE.2018.8457352
DO - 10.1109/JCSSE.2018.8457352
M3 - Conference contribution
AN - SCOPUS:85057755102
T3 - Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018
BT - Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018
A2 - Sawangphol, Wudhichart
A2 - Mitrpanont, Jarernsri
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018
Y2 - 11 July 2018 through 13 July 2018
ER -