| Summary: | Although Internet of Things (IoT) has been recently receiving attention from the research community, undoubtedly, there still exists several privacy concerns about those devices. In particular, IoT devices in the cyberspace are reachable and visible through IP addresses. This article uniquely exploits to qualify the distribution of owner information of IoT devices based on the observation; consumers may write relevant details into the application-layer service on the IoT devices, such as company or usernames. We propose to automatically extract owner annotation by utilizing a set of techniques (network scanning, machine learning, and natural language processing). We use the probing and classifier to determine whether the response data come from an IoT device. The natural language-processing technique is used to extract owner information from IoT devices. We have conducted real-world experiments to evaluate our integrated approach empirically. The results show that the precision is 97% and the coverage is 96%. Furthermore, our approach is running on a more larger unlabeled dataset consisting of 93 million response packets from the whole IPv4 space. Our analysis has drawn upon nearly 4.3 million IoT devices exposed to the public, and it is a typical trail effect of the owner information distribution.
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