Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2022-07-19
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
629-634
Series
2022 International Wireless Communications and Mobile Computing, IWCMC 2022
Abstract
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.
Description
Funding Information: 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 RandD program of China under Grant No.2018YFB2100400 and the national science foundation of China under Grant No.61972308 Publisher Copyright: © 2022 IEEE. | openaire: EC/H2020/871808/EU//INSPIRE-5Gplus
Keywords
Beyond 5G, Convolutional Neural Network (CNN), GPS spoofing, Transfer Learning, Unmanned Aerial Vehicles (UAVs)
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Citation
Dang , Y , Benzaid , C , Taleb , T , Yang , B & Shen , Y 2022 , Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs . in 2022 International Wireless Communications and Mobile Computing, IWCMC 2022 . International Wireless Communications and Mobile Computing Conference , IEEE , pp. 629-634 , International Wireless Communications and Mobile Computing Conference , Dubrovnik , Croatia , 30/05/2022 . https://doi.org/10.1109/IWCMC55113.2022.9824124