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Nowadays, increasing number of intelligent devices and smart sensors are connected by Internet of Things (IoT) techniques, and have helped people to manage and improve their lives. However, security issues are emerging in IoT, among which things identification is one of the challenges in that various solutions of different vendors, standards, protocols and communities groups coexist. In this paper, we address the challenge of IoT device identification by analyzing a sequence of packets from its high-level network traffic, i.e., network-flow data and extract unique flow-based features to create a fingerprint for each device. We adopt supervised machine learning techniques for the identification task. The proposed approach can automatically identify white-listed device types and individual device instances connected to a network. Moreover, we propose a security system model design that enables enforcement of …