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

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorDang, Yongchao
dc.contributor.authorBenzaid, Chafika
dc.contributor.authorYang, Bin
dc.contributor.authorTaleb, Tarik
dc.contributor.authorShen, Yulong
dc.contributor.departmentDepartment of Communications and Networking
dc.contributor.departmentUniversity of Oulu
dc.contributor.departmentChuzhou University
dc.contributor.departmentXidian University
dc.contributor.departmentDepartment of Communications and Networkingen
dc.date.accessioned2023-01-02T09:29:34Z
dc.date.available2023-01-02T09:29:34Z
dc.date.issued2022-12-15
dc.descriptionPublisher Copyright: Author
dc.description.abstractUnmanned 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 causedby 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.en
dc.description.versionPeer revieweden
dc.format.extent18
dc.format.extent25068-25085
dc.format.mimetypeapplication/pdf
dc.identifier.citationDang , 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.2022.3195320en
dc.identifier.doi10.1109/JIOT.2022.3195320
dc.identifier.issn2327-4662
dc.identifier.otherPURE UUID: c14ad608-3990-462e-95a2-f6b074909185
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/c14ad608-3990-462e-95a2-f6b074909185
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85135752617&partnerID=8YFLogxK
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/96058764/Deep_Ensemble_Learning_Based_GPS_Spoofing_Detection_for_Cellular_Connected_UAVs.pdf
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/118697
dc.identifier.urnURN:NBN:fi:aalto-202301021059
dc.language.isoenen
dc.publisherIEEE
dc.relation.ispartofseriesIEEE Internet of Things Journalen
dc.relation.ispartofseriesVolume 9, issue 24en
dc.rightsopenAccessen
dc.subject.keywordAutonomous aerial vehicles
dc.subject.keywordBase stations
dc.subject.keywordDeep ensemble learning
dc.subject.keywordEncryption
dc.subject.keywordGlobal Positioning System
dc.subject.keywordGPS spoofing
dc.subject.keywordMulti-Layer Perceptron (MLP)
dc.subject.keywordNavigation
dc.subject.keywordPath loss
dc.subject.keywordReceivers
dc.subject.keywordServers
dc.subject.keywordUAV
dc.titleDeep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
Files