Multi-Level IoT Device Identification

Abstract

The rapid development of the Internet of Things (IoT) has brought challenges to IoT platforms for high-efficiency deployments and low-budget management. Identifying IoT devices is the prerequisite for monitoring, protecting, and managing them. Considering different providers and IoT device renovation, centralized device identification solutions require large amounts of training data and frequent model updates. Traditional solutions based on machine learning cannot preserve identification precision for the long term at a low cost in reality. In this paper, we propose a multi-level IoT device identification framework, alleviating the problem of novel class detection and large-scale updating of IoT models in IoT device identification. The proposed framework improves the usability of device identification technology in the real world. We also designed an IoT device identification method, achieving an average identification accuracy of 93.37 %. With this proposed multi-level IoT device identification framework, IoT device identification can achieve a high precision over a long time.