Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-12-15
Major/Subject
Mcode
Degree programme
Language
en
Pages
18
25068-25085
Series
IEEE Internet of Things Journal, Volume 9, issue 24
Abstract
Unmanned Aerial Vehicles (UAVs) are an emerging technology in the 5G and beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the open-source simulator can use commercial software-defined radio tools to generate fake GPS signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations at the same time. In this paper, we propose a novel deep ensemble learning-based, mobile network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between base stations and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multi-layer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.
Description
Publisher Copyright: Author
Keywords
Autonomous aerial vehicles, Base stations, Deep ensemble learning, Encryption, Global Positioning System, GPS spoofing, Multi-Layer Perceptron (MLP), Navigation, Path loss, Receivers, Servers, UAV
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Citation
Dang, Y, Benzaid, C, Yang, B, Taleb, T & Shen, Y 2022, ' Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs ', IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25068-25085 . https://doi.org/10.1109/JIOT.2022.3195320