University of Oulu

Y. Dang, C. Benzaïd, B. Yang and T. Taleb, "Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems," 2021 International Conference on Networking and Network Applications (NaNA), 2021, pp. 501-506, doi: 10.1109/NaNA53684.2021.00093

Deep learning for GPS spoofing detection in cellular-enabled UAV systems

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Author: Dang, Yongchao1; Benzaïd, Chafika1; Yang, Bin1;
Organizations: 1Aalto University, Espoo, Finland
2University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-03-15


Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV’s position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position. Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.

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ISBN: 978-1-6654-4158-2
ISBN Print: 978-1-6654-4159-9
Pages: 501 - 506
DOI: 10.1109/NaNA53684.2021.00093
Host publication: 2021 International Conference on Networking and Network Applications, NaNA 2021
Conference: International Conference on Networking and Network Applications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was partially supported by the European Union’s Horizon 2020 Research and Innovation Program through the 5G!Drones Project under Grant No. 857031, the Academy of Finland 6Genesis project under Grant No. 318927 and the INSPIRE-5Gplus project under Grant No. 871808. It was also supported in the National Outstanding Youth Science Fund Project of China with grant No. 61825104 and the National Natural Science Foundation of China under grant agreement No. 61941105.
EU Grant Number: (857031) 5G!Drones - Unmanned Aerial Vehicle Vertical Applications’ Trials Leveraging Advanced 5G Facilities
(871808) INSPIRE-5Gplus - INtelligent Security and PervasIve tRust for 5G and Beyond
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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