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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    Title :

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


    Contributors:
    Wang, Jiushuang (author) / Liu, Ying (author) / Su, Wei (author) / Feng, Huifen (author)


    Publication date :

    2020-11-01


    Size :

    3819588 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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