A DDoS attack detection based on deep learning in software-defined Internet of things
Jiushuang Wang, Ying Liu, Wei Su, Huifen Feng · 2020
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.