Deep multi-locality convolutional neural network for DDoS detection in smart home IoT

Internet of things (IoT) devices usually offer limited resources such as processing, memory, and network capacity, bringing more security threats to the environment. Distributed denial of service (DDoS) signal attacks are among the most serious threats. Software-defined networking (SDN) is a promising paradigm that could offer a scalable security solution optimised for the IoT ecosystem. However, investigating a robust security solution is still one of the most challenging problems that a smart home environment faces in SDN. In this paper, we introduce a multi-locality deep learning model for the detection of DDoS signals in an SDN-based smart home. It employs convolutional neural networks (CNNs) by learning different levels of local information from the data. In this work, an ensemble of two CNNs to detect malicious traffic flows with low computation overhead framework is proposed. Experimental results demonstrate the robustness, effectiveness, and efficiency of our solution in detecting DDoS attacks in SDN smart home.