Acquisitional Rule-based Engine for Discovering Internet-of-Things Devices

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 pre-requisite 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 large training data with labels. Therefore, automatic device discovery and annotation in large-scale remains an open problem in IoT. In this paper, we propose an Acquisitional Rule-based Engine (ARE), which can automatically generate rules for discovering and annotating IoT devices without any training data. ARE builds device rules by leveraging application-layer response data from IoT devices and product descriptions in relevant websites for device annotations. We define a transaction as a mapping between a unique response to a product description. To collect the transaction set, ARE extracts relevant terms in the response data as the search queries for crawling websites. ARE uses the association algorithm to generate rules of IoT device annotations in the form of (type, vendor, and product). We conduct experiments and three applications to validate the effectiveness of ARE.