Self-Supervised Learning IoT Device Features With Graph Contrastive Neural Network for Device Classification in Social Internet of Things

Abstract

Device Classification (DC) is one of the critical network management means in the Internet of Things (IoT). Most of machine learning-driven DC methods are implemented in two steps, i.e., extracting device features and then training the classifier. Thus, for the same classifier, the quality of device features plays a decisive role. However, for one thing, these methods usually merely consider device traffic or device attributed features, ignoring the complex relationship between devices in Social IoT. For another thing, these features fed to the classifier are often selected manually without automatic learning. These two factors seriously affect the performance and flexibility of the algorithm. To address the above issues, we propose a novel Graph Contrastive Neural Network-based algorithm for DC dubbed GCNNDC. Specifically, based on the complex relationship between heterogeneous objects in Social IoT and the attribute information of the device itself, we first construct four kinds of homogeneous device-device attributed networks as the input of the neural network. Then, we train a novel GCNN model to learn informative device features in a self-supervised learning fashion. Finally, we leverage the features to train a classifier to complete the DC task. Experimental results on real-world IoT network dataset demonstrate the superiority of GCNNDC.