University of Oulu

Y. Dang, C. Benzaïd, T. Taleb, B. Yang and Y. Shen, "Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs," 2022 International Wireless Communications and Mobile Computing (IWCMC), 2022, pp. 629-634, doi: 10.1109/IWCMC55113.2022.9824124.

Transfer learning based GPS spoofing detection for cellular-connected UAVs

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Author: Dang, Yongchao1; Benzaïd, Chafika2; Taleb, Tarik2;
Organizations: 1Aalto University, Espoo, Finland
2University of Oulu, Oulu, Finland
3Chuzhou University, Chuzhou, China
4Xidian University, Xi’an, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-09-26


Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs’ locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.

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Series: International Wireless Communications & Mobile Computing Conference
ISSN: 2376-6492
ISSN-L: 2376-6492
ISBN: 978-1-6654-6749-0
ISBN Print: 978-1-6654-6750-6
Pages: 629 - 634
DOI: 10.1109/IWCMC55113.2022.9824124
Host publication: 2022 International wireless communications and mobile computing (IWCMC), 30 May 2022 - 03 June 2022, Dubrovnik, Croatia
Conference: International Wireless Communications and Mobile Computing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The research work presented in this paper was partially supported by the European Union's Horizon 2020 Research and Innovation Program through the INSPIRE-5Gplus project under Grant No. 871808. It was also partially supported by the national key R&D program of China under Grant No. 2018YFB2100400 and the national science foundation of China under Grant No. 61972308.
EU Grant Number: (871808) INSPIRE-5Gplus - INtelligent Security and PervasIve tRust for 5G and Beyond
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