Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

Yongchao Dang, Chafika Benzaid, Bin Yang, Tarik Taleb

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on Networking and Network Applications, NaNA 2021
Number of pages6
ISBN (Electronic)978-1-6654-4158-2
Publication statusPublished - 10 Dec 2021
MoE publication typeA4 Conference publication
EventInternational Conference on Networking and Network Applications - Lijiang City, China
Duration: 29 Oct 20211 Nov 2021


ConferenceInternational Conference on Networking and Network Applications
Abbreviated titleNaNa
CityLijiang City
Internet address


  • Deep Learning
  • GPS spoofing
  • MLP
  • Path Loss
  • UAV


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