WebIoT: Classifying Internet of Things Devices at Internet Scale through Web Characteristics

Abstract

The number of Internet of Things (IoT) devices connected to the Internet has been growing rapidly. Such a large number of IoT devices bring significant challenges to device man-agement and cyberspace security. The discovery and classification of IoT devices are the prerequisites for monitoring and protecting them. However, existing Internet-scale IoT device classification methods mainly rely on textual analysis of the device response data, whose performance can be affected by the complexity or the multilingualism of the response texts. In this paper, we propose WebIoT, which mainly utilizes the image characteristics of the IoT devices' web interfaces to classify them for the first time. We leverage the observation that many IoT devices have web interfaces for device configuration and device status display, whose visual presentations contain abundant characteristics for device classification. Experiment results show that our method achieves 95.4% precision and 91.5% recall, which significantly outperforms other text analysis-based methods.