IoT Inspector: Crowdsourcing Labeled Network Traffic from Smart Home Devices at Scale
This addresses the data scarcity problem for researchers in smart home security, privacy, and other domains, though it is incremental as it builds on existing crowdsourcing and data collection methods.
The authors tackled the lack of large-scale labeled network traffic data from real-world smart home devices by crowdsourcing it using IoT Inspector, resulting in a dataset from 44,956 devices that revealed security and privacy issues, such as outdated TLS versions and 350 third-party tracking domains on smart TVs.
The proliferation of smart home devices has created new opportunities for empirical research in ubiquitous computing, ranging from security and privacy to personal health. Yet, data from smart home deployments are hard to come by, and existing empirical studies of smart home devices typically involve only a small number of devices in lab settings. To contribute to data-driven smart home research, we crowdsource the largest known dataset of labeled network traffic from smart home devices from within real-world home networks. To do so, we developed and released IoT Inspector, an open-source tool that allows users to observe the traffic from smart home devices on their own home networks. Since April 2019, 4,322 users have installed IoT Inspector, allowing us to collect labeled network traffic from 44,956 smart home devices across 13 categories and 53 vendors. We demonstrate how this data enables new research into smart homes through two case studies focused on security and privacy. First, we find that many device vendors use outdated TLS versions and advertise weak ciphers. Second, we discover about 350 distinct third-party advertiser and tracking domains on smart TVs. We also highlight other research areas, such as network management and healthcare, that can take advantage of IoT Inspector's dataset. To facilitate future reproducible research in smart homes, we will release the IoT Inspector data to the public.