TY - GEN
T1 - A Machine Learning Approach for Detecting Unauthorized Drone Operators
AU - Ahmad, Abdulrahman
AU - Alameri, Sultan
AU - Ibrahim, Youssef
AU - Marzouqi, Hasan Al
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Governmental authorities worldwide have always been striving to impose further restrictions on the use of drones for safety reasons. On the other hand, hijackers always try to find a way to control the UAV to steal it, damage it or even assign tasks that are not meant to be operated. Classification of the UAV operators from the driving behavior is crucial to prevent malicious attacks and hijacking. This paper proposes a short-time prediction of unauthorized pilot behavior. A comprehensive analysis is conducted to find the optimal machine learning approach to classify the UAV operator in terms of accuracy, sensitivity, and prediction time. The utilized dataset consists of recorded flying sessions of 20 different pilots based on four features, thrust, yaw, pitch, and roll. To balance the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized. A time-series forecasting approach is proposed to recognize repetitive patterns of pilot behavior over short time intervals. Finally, the proposed decision tree (DT) algorithm achieved the highest accuracy with 95% at a prediction time of 15 ms. The results outperformed state-of-the-art solutions for unauthorized pilot detection.
AB - Governmental authorities worldwide have always been striving to impose further restrictions on the use of drones for safety reasons. On the other hand, hijackers always try to find a way to control the UAV to steal it, damage it or even assign tasks that are not meant to be operated. Classification of the UAV operators from the driving behavior is crucial to prevent malicious attacks and hijacking. This paper proposes a short-time prediction of unauthorized pilot behavior. A comprehensive analysis is conducted to find the optimal machine learning approach to classify the UAV operator in terms of accuracy, sensitivity, and prediction time. The utilized dataset consists of recorded flying sessions of 20 different pilots based on four features, thrust, yaw, pitch, and roll. To balance the dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is utilized. A time-series forecasting approach is proposed to recognize repetitive patterns of pilot behavior over short time intervals. Finally, the proposed decision tree (DT) algorithm achieved the highest accuracy with 95% at a prediction time of 15 ms. The results outperformed state-of-the-art solutions for unauthorized pilot detection.
UR - http://www.scopus.com/inward/record.url?scp=85167415131&partnerID=8YFLogxK
U2 - 10.1109/ASET56582.2023.10180485
DO - 10.1109/ASET56582.2023.10180485
M3 - Conference contribution
AN - SCOPUS:85167415131
T3 - 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
BT - 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023
Y2 - 20 February 2023 through 23 February 2023
ER -