Y. Dang, C. Benzaïd, B. Yang, T. Taleb and Y. Shen, "Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3195320.
Deep ensemble learning based GPS spoofing detection for cellular-connected UAVs
|Author:||Dang, Yongchao1; Benzaïd, Chafika2; Yang, Bin3;|
1Department of Communications and Networking, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland
2Information Technology and Electrical Engineering, Oulu University, 90570 Oulu, Finland
3School of Computer and Information Engineering, Chuzhou University, 239000 Anhui, China
4Department of Computer and Information Security, Sejong University, Seoul, 05006 South Korea
5School of Computer Science and Technology, Xidian University, 710071 Shaanxi, China
|Online Access:||PDF Full Text (PDF, 1.5 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022092660177
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-09-26
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.
IEEE internet of things journal
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
113 Computer and information sciences
This work was supported by the European Union’s Horizon 2020 Research and Innovation Program through the 5G!Drones Project under Grant 857031, the INSPIRE-5Gplus project under Grant 871808, the Academy of Finland 6Genesis project under Grant 318927, and the National Natural Science Foundation of China under Grants 61972308, 61941114 and 61962033.
|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 (Academy of Finland Funding decision)
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