Device Type Identification via Network Traffic and Lightweight Convolutional Neural Network for Internet of Things
Guangwei Qing, Huifang Wang, Liang Guo, Jie Yang · IEEE Access · 2020
Device type identification (DTI) is one of the most important techniques for the management of Internet of things (IoT). Recently, deep learning (DL) has been considered as a powerful tools for classification or identification, and some researches have introduced DL into DTI for advanced performance. However, DL-based DTI generally has high computational complexity and large model sizes, which are unsuitable for IoT. Thus, we proposed a lightweight convolutional neural network-based DTI with low computational complexity and few model sizes. In detail, we remove the redundant fully-connected layers and replace common convolution with separable convolution. Simulation results demonstrate that lightweight CNN-based DTI just has less than 5% of computational complexity and model size of CNN-based DTI, though there are less than 0.5% performance gap between them.