The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets. In this paper, we address this challenge by developing a framework for IoT device classification using network traffic characteristics. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of 6 months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns and cipher suites. Third, we develop a multi-stage machine-learning-based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99%. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.
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