Machine Learning based GNSS Spoofing Detection and Mitigation for Cellular-Connected UAVs

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.advisorJäntti, Riku, Prof., Aalto University, Department of Communications and Networking, Finland
dc.contributor.authorDang, Yongchao
dc.contributor.departmentInformaatio- ja tietoliikennetekniikan laitosfi
dc.contributor.departmentDepartment of Information and Communications Engineeringen
dc.contributor.labCommunication Engineeringen
dc.contributor.schoolSähkötekniikan korkeakoulufi
dc.contributor.schoolSchool of Electrical Engineeringen
dc.contributor.supervisorJäntti, Riku, Prof., Aalto University, Department of Communications and Networking, Finland
dc.date.accessioned2023-09-01T09:00:13Z
dc.date.available2023-09-01T09:00:13Z
dc.date.defence2023-09-15
dc.date.issued2023
dc.description.abstractCellular-connected Unmanned Aerial Vehicle (UAV) systems are a promising paradigm in the upcoming 5G-and-beyond mobile cellular networks by delivering numerous applications, such as the transportation of medicine, building inspection, and emergency communication. With the help of a cellular communication system and Global Navigation Satellite System (GNSS), UAVs can be deployed independently or collectively in remote and densely populated areas on demand. However, the civil GNSS service, especially GPS, is unencrypted and vulnerable to spoofing attacks, which threatens the security of remotely- and autonomously-controlled UAVs. Indeed, a GPS spoofer can use commercial Software-Defined Radio (SDR) tools to generate fake GPS signals and fool the UAV GPS receiver to calculate the wrong locations. Fortunately, the 3rd Generation Partnership Project (3GPP) has initiated a set of techniques and supports that enable mobile cellular networks to track and identify UAVs in order to enhance low-altitude airspace security. The research works in this thesis leverage the potential of machine learning methods and 3GPP technique support to detect and mitigate GPS spoofing attacks for cellular-connected UAVs. The contributions of this thesis contain four parts. First, we propose a new adaptive trustable residence area algorithm to improve the conventional Mobile Positioning System (MPS) in terms of GPS spoofing detection accuracy under three base stations. Then, we deploy deep neural networks in the Multi-access Edge Computing (MEC) based 5G-assisted Unmanned Aerial System (UAS) for detecting GPS spoofing attacks, where the proposed deep learning methods can detect GPS spoofing attacks with only a single base station. Next, we analyze the max-min transmission rate for cellular-connected UAVs system theoretically and design an optimal Graphic Neural Network (GNN) to detect GPS spoofing attacks for cellular-connected UAV swarms. Finally, we employ a 3D radio map and particle filter to recover the UAV position and mitigate GPS spoofing attacks.en
dc.format.extent100 + app. 82
dc.format.mimetypeapplication/pdfen
dc.identifier.isbn978-952-64-1395-2 (electronic)
dc.identifier.isbn978-952-64-1394-5 (printed)
dc.identifier.issn1799-4942 (electronic)
dc.identifier.issn1799-4934 (printed)
dc.identifier.issn1799-4934 (ISSN-L)
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/123015
dc.identifier.urnURN:ISBN:978-952-64-1395-2
dc.language.isoenen
dc.opnVälisuo, Petri, Prof., University of Vaasa, Finland
dc.publisherAalto Universityen
dc.publisherAalto-yliopistofi
dc.relation.haspart[Publication 1]: Dang, C. Benzaïd, Y. Shen and T. Taleb. GPS spoofing detector with adaptive trustable residence area for cellular based-UAVs. In 2020 IEEE Global Communications Conference, Taipei, Taiwan, pp. 1-6, Dec 2020. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202103102244. DOI: 10.1109/GLOBECOM42002.2020.9348030
dc.relation.haspart[Publication 2]: Y. Dang, C. Benzaïd, B. Yang and T. Taleb. Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems. In 2021 International Conference on Networking and Network Applications (NaNA), Lijiang City, China, pp. 501-506, Dec 2021
dc.relation.haspart[Publication 3]: Y. Dang, C. Benzaïd, T. Taleb, B. Yang, and Y. Shen. Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs. In 2022 International Wireless Communications and Mobile Computing (IWCMC), Dubrovnik, Croatia, pp. 629-634, July 2022. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202208174909. DOI: 10.1109/IWCMC55113.2022.9824124
dc.relation.haspart[Publication 4]: Y. Dang, C. Benzaïd, B. Yang, T. Taleb and Y. Shen. Deep Ensemble Learning Based GPS Spoofing Detection for Cellular-Connected UAVs. IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25068-25085, Dec 2022. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202301021059. DOI: 10.1109/JIOT.2022.3195320
dc.relation.haspart[Publication 5]: B. Yang, Y. Dang, T. Taleb, S. Shen and X. Jiang. Sum Rate and Max-Min Rate for Cellular-Enabled UAV Swarm Networks. IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 1073-1083, Jan. 2023. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202302282249. DOI: 10.1109/TVT.2022.3204624
dc.relation.haspart[Publication 6]: Y. Dang, A. Karakoc and R. Jäntti. Graphic Neural Network based GPS Spoofing Detection for Cellular-Connected UAV Swarm. In 2023 IEEE 97th Vehicular Technology Conference, Florence, Italy, June 2023. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202308235032. DOI: 10.1109/VTC2023-Spring57618.2023.10200557
dc.relation.haspart[Publication 7]: Y. Dang, A. Karakoc, S. Norshahida, and R. Jantti. 3D Radio Mapbased GPS spoofing Detection and Mitigation for Cellular-Connected UAVs. Submitted to IEEE Transactions on Machine Learning in Communications and Networking, Nov 2022
dc.relation.ispartofseriesAalto University publication series DOCTORAL THESESen
dc.relation.ispartofseries130/2023
dc.revLohan, Simona, Prof., Tampere University, Finland
dc.revGaleazzi, Roberto, Prof., Technical University of Denmark, Denmark
dc.subject.keywordUAVen
dc.subject.keywordGNSSen
dc.subject.keywordGPS spoofingen
dc.subject.keywordmachine learningen
dc.subject.otherCommunicationen
dc.titleMachine Learning based GNSS Spoofing Detection and Mitigation for Cellular-Connected UAVsen
dc.typeG5 Artikkeliväitöskirjafi
dc.type.dcmitypetexten
dc.type.ontasotDoctoral dissertation (article-based)en
dc.type.ontasotVäitöskirja (artikkeli)fi
local.aalto.acrisexportstatuschecked 2023-09-15_1512
local.aalto.archiveyes
local.aalto.formfolder2023_08_31_klo_13_55
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