Unmanned Aerial Vehicles (UAVs) are increasingly integral in various sectors, simultaneously encountering rising security threats as UAV and Urban Air Mobility (UAM) networks continue to expand. This paper addresses the challenge of securing UAM networks while also emphasizing generalizability of the security solution to protect heterogeneous UAVs against threats that compromise their stability, reliability and can cause catastrophic failures such as a crash landing. The deployment of traditional machine learning (ML) based intrusion detection systems (IDSs) is often hampered in real-world applications due to a lack of generalizability of the security solution. As a result, the system fails to provide adequate security across the varying models and platforms of UAVs, each with its unique statistical properties and data distributions. To address these challenges, we focus on employing a comprehensive set of UAV sensor parameters, tailored feature engineering and selection to develop multi-stage cross-validated ensemble learning systems to facilitate generalized detection of attack and non-attack cases. For additional analysis, we cross-validate the models using two different cross-validation techniques. The proposed stacking ensemble systems provide the overall best performance, with AUC within the range of 92% to 100% across different cross-validations.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    GIIDS: Generalized Intelligent Intrusion Detection System for Heterogeneous UAVs in UAM


    Beteiligte:
    Kabir, Fahmina (Autor:in) / Mowla, Nishat I (Autor:in) / Doh, Inshil (Autor:in)


    Erscheinungsdatum :

    16.02.2025


    Format / Umfang :

    6931114 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Leveraging UAVs for Rapid and Hierarchical Railway Intrusion Detection

    Tong, Lei / Wang, Zhipeng / Qin, Yong et al. | Springer Verlag | 2024


    Intelligent Detection System for Spoofing and Jamming Attacks in UAVs

    Jasim, Khadeeja Sabah / Ali Alheeti, Khattab M. / Najem Alaloosy, Abdul Kareem A. | Springer Verlag | 2023


    INTELLIGENT AUTONOMY FOR UAVs

    Pawlowski, A. / Pridmore, L. / Franke, J. et al. | British Library Conference Proceedings | 2003


    Mission planning for heterogeneous tasks with heterogeneous UAVs

    Wang, J. J. / Zhang, Y. F. / Geng, L. et al. | IEEE | 2014


    Intelligent Control Approaches for UAVs

    KrisnaKumar, Kalmanje | NTRS | 2003