TY - GEN
T1 - Drone Detection Approach Based on Radio-Frequency Using Convolutional Neural Network
AU - Al-Emadi, Sara
AU - Al-Senaid, Felwa
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Recently, Unmanned Aerial Vehicles, also known as drones, are becoming rapidly popular due to the advancement of their technology and the significant decrease in their cost. Although commercial drones have proven their effectiveness in many day to day applications such as cinematography, agriculture monitoring and search and rescue, they are also being used in malicious activities that are targeting to harm individuals and societies which raises great privacy, safety and security concerns. In this research, we propose a new drone detection solution based on the Radio Frequency (RF) emitted during the live communication session between the drone and its controller using a Deep Learning (DL) technique, namely, the Convolutional Neural Network (CNN). The results of the study have proven the effectiveness of using CNN for drone detection with accuracy and F1 score of over 99.7% and drone identification with accuracy and F1 score of 88.4%. Moreover, the results yielded from this experiment have outperformed those reported in the literature for RF based drone detection using Deep Neural Networks.
AB - Recently, Unmanned Aerial Vehicles, also known as drones, are becoming rapidly popular due to the advancement of their technology and the significant decrease in their cost. Although commercial drones have proven their effectiveness in many day to day applications such as cinematography, agriculture monitoring and search and rescue, they are also being used in malicious activities that are targeting to harm individuals and societies which raises great privacy, safety and security concerns. In this research, we propose a new drone detection solution based on the Radio Frequency (RF) emitted during the live communication session between the drone and its controller using a Deep Learning (DL) technique, namely, the Convolutional Neural Network (CNN). The results of the study have proven the effectiveness of using CNN for drone detection with accuracy and F1 score of over 99.7% and drone identification with accuracy and F1 score of 88.4%. Moreover, the results yielded from this experiment have outperformed those reported in the literature for RF based drone detection using Deep Neural Networks.
KW - CNN
KW - Convolutional Neural Networks
KW - DL
KW - Deep learning
KW - Drone Detection
KW - Drone Identification
KW - Infrastructure Security
KW - ML
KW - RF
KW - Radio Frequency
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85085478429&partnerID=8YFLogxK
U2 - 10.1109/ICIoT48696.2020.9089489
DO - 10.1109/ICIoT48696.2020.9089489
M3 - Conference contribution
AN - SCOPUS:85085478429
T3 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
SP - 29
EP - 34
BT - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Y2 - 2 February 2020 through 5 February 2020
ER -