Cognitive radio (CR)-based internet of things systems can be considered as an efficient solution for futuristic smart technologies. However, CRs are naturally vulnerable to two major security threats; primary user emulation (PUE) and jamming attacks. Machine learning has been recently applied to the detection of these attacks. Still, the need for feature extraction required by machine learning techniques restrains the full exploitation of raw data. To alleviate this need, this paper proposes one-dimensional deep learning as a framework for identifying such attacks. Simulations show the ability of the proposed algorithm to detect these attacks with high performance.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Deep Learning-Assisted Detection of PUE and Jamming Attacks in Cognitive Radio Systems


    Beteiligte:
    Aygul, Mehmet Ali (Autor:in) / Furqan, Haji M (Autor:in) / Nazzal, Mahmoud (Autor:in) / Arslan, Huseyin (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    1441875 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Jamming Attacks Against Control Systems: A Survey

    Dong, Yanbo / Zhou, Peng | Springer Verlag | 2017


    Bandwidth-Efficient Frequency Hopping based Anti-Jamming Game for Cognitive Radio assisted Wireless Sensor Networks

    Ibrahim, Khalid / Qureshi, Ijaz Mansoor / Naveed Malik, Adqas et al. | IEEE | 2021


    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


    On Jamming Attacks in Crowdsourced Air Traffic Surveillance

    Leonardi, Mauro / Strohmeier, Martin / Lenders, Vincent | IEEE | 2021