Towards automatic fingerprinting of IoT devices in the cyberspace

Abstract

Nowadays, the cyberspace consists of an increasing number of IoT devices, such as net-printers, webcams, and routers. Illuminating the nature of online devices would provide insights into detecting potentially vulnerable devices on the Internet. However, there is a lack of device discovery in large-scale due to the massive number of device models (i.e., types, vendors, and products). In this paper, we propose an efficient approach to generate fingerprints of IoT devices. We observe that device manufacturers have different network system implementations on their products. We explore features spaces of IoT devices in three network layers, including the network-layer, transport-layer, and application-layer. Utilizing the feature of network protocols, we generate IoT device fingerprints based on neural network algorithms. Furthermore, we implement the prototype system and conduct real experiments to validate the performance of device fingerprints. Results show that our classification can generate device class labels with a 94% precision and 95% recall. We use those device fingerprints to discover 15.3 million network-connected devices and analyze their distribution characteristics in cyberspace.