With the popularity of Internet of Things (IoT) applications, security has become extremely important. A recent distributed denial-of-service (DDoS) attack revealed vulnerabilities that are prevalent in IoT, and many IoT devices accidentally contributed to the DDoS attack. software-defined network provides a way to securely manage IoT devices. In this paper, we first present a general framework for software-defined Internet of Things (SD-IoT). The proposed framework consists of a SD-IoT controller, SD-IoT switches integrated with an IoT gateway, and IoT devices. We then propose a deep learning detection algorithm based on time series using the proposed SD-IoT framework. Finally, experimental results show that the proposed algorithm has good performance.


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    Titel :

    A DDoS attack detection based on deep learning in software-defined Internet of things


    Beteiligte:
    Wang, Jiushuang (Autor:in) / Liu, Ying (Autor:in) / Su, Wei (Autor:in) / Feng, Huifen (Autor:in)


    Erscheinungsdatum :

    01.11.2020


    Format / Umfang :

    3819588 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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