The cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as integral components of the 5G and beyond system due to their mobility and flexibility. Compared to a traditional single UAV, a flock of UAVs established as a UAV swarm can implement diverse distributed applications economically and efficiently, such as cooperatively smart agriculture, joint search and rescue, and supplementing temporary network connections. However, the GPS spoofing attack can manipulate UAV swarm locations and distort UAV swarm topology, which threatens the security of swarm communication and control. This paper proposes a Graphic Neural Networks (GNN) based GPS spoofing detection approach for cellular-connected UAV swarms. Especially, we propose a system in which the GNN model is used to detect GPS spoofing attacks by analyzing the similarity between the swarm GPS topology and communications topology. To evaluate the proposed neural networks, we use a bipartite graph and Hungarian algorithm to build a UAV swarm simulator. The results show that GNN can efficiently compute topologies’ similarity and detect GPS spoofing attacks. For instance, for a UAV swarm consisting of 10 UAVs, GNN detects the spoofing with accuracy over 90% and computation time of fewer than 10 milliseconds using Intel Core 1.6 GHz processor.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Graphic Neural Network based GPS Spoofing Detection for Cellular-Connected UAV swarm


    Beteiligte:
    Dang, Yongchao (Autor:in) / Karakoc, Alp (Autor:in) / Jantti, Riku (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    4383886 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Spoofing detection

    LAWLIS JAMES MARTIN | Europäisches Patentamt | 2016

    Freier Zugriff

    SPOOFING DETECTION

    LAWLIS JAMES MARTIN | Europäisches Patentamt | 2016

    Freier Zugriff

    CAN network spoofing

    THOMAS KEITH S / MOWER DAN C / EBUEN RYAN B | Europäisches Patentamt | 2016

    Freier Zugriff

    Deep learning for GPS spoofing detection in cellular-enabled UAV systems

    Dang, Y. (Yongchao) / Benzaïd, C. (Chafika) / Yang, B. (Bin) et al. | BASE | 2021

    Freier Zugriff