Discovery of Internet of Thing devices based on rules
Qiang Li, Xuan Feng, Haining Wang, Limin Sun · 2018
The rapidly increasing landscape of Internet of Thing (IoT) devices has introduced significant technical challenges for their management and security, as these IoT devices in the wild are from different device types, vendors, and product models. The discovery of IoT devices is the prerequisite to characterize, monitor, and protect these devices. However, manual device annotation impedes a large-scale discovery, and the device classification based on machine learning requires a large training data with labels. In this paper, we propose the discovery and recognition of IoT devices based on rules. Specifically, the web crawler obtains the description webpages about IoT devices, and natural language processing technique extracts device annotations. We propose the association algorithm to generate rules of IoT device annotations in the form of (type, vendor, and product). Our preliminary experimental results show that the rules can achieve a precision of 96% and coverage over 95%. We have generated 582,328 rules, that is two orders of magnitude larger than those of the state-of-the-art tools.