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

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School of Electrical Engineering | Doctoral thesis (article-based) | Defence date: 2023-09-15
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Author
Date
2023
Department
Informaatio- ja tietoliikennetekniikan laitos
Department of Information and Communications Engineering
Major/Subject
Mcode
Degree programme
Language
en
Pages
100 + app. 82
Series
Aalto University publication series DOCTORAL THESES, 130/2023
Abstract
Cellular-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.
Description
Supervising professor
Jäntti, Riku, Prof., Aalto University, Department of Communications and Networking, Finland
Thesis advisor
Jäntti, Riku, Prof., Aalto University, Department of Communications and Networking, Finland
Keywords
UAV, GNSS, GPS spoofing, machine learning
Parts
  • [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 View at publisher
  • [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
  • [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 View at publisher
  • [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 View at publisher
  • [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 View at publisher
  • DOI: 10.1109/VTC2023-Spring57618.2023.10200557 View at publisher
  • [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
Citation
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https://urn.fi/URN:ISBN:978-952-64-1395-2
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[diss] Sähkötekniikan korkeakoulu / ELEC