TY - GEN
T1 - A Multi-Model Attack Detection Scheme for the UAV Navigation System
AU - Bni, Asmae
AU - Yin, Shen
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The navigation system of an unmanned aerial vehicle receives data inputs from multiple sources. The data inputs influence the steering and the path planning of the vehicle. Therefore, the data used by the navigation system should be monitored to detect potential attacks. The suggested attack detection scheme is based on unsupervised learning methods to classify intentional anomalies. Furthermore, it is based on multiple types of inputs to increase the detection coverage.
AB - The navigation system of an unmanned aerial vehicle receives data inputs from multiple sources. The data inputs influence the steering and the path planning of the vehicle. Therefore, the data used by the navigation system should be monitored to detect potential attacks. The suggested attack detection scheme is based on unsupervised learning methods to classify intentional anomalies. Furthermore, it is based on multiple types of inputs to increase the detection coverage.
UR - https://www.scopus.com/pages/publications/85205676980
U2 - 10.1109/ICIEA61579.2024.10665287
DO - 10.1109/ICIEA61579.2024.10665287
M3 - 会议稿件
AN - SCOPUS:85205676980
T3 - 2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024
BT - 2024 IEEE 19th Conference on Industrial Electronics and Applications, ICIEA 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE Conference on Industrial Electronics and Applications, ICIEA 2024
Y2 - 5 August 2024 through 8 August 2024
ER -