![]() Shabtai, "Kitsune: an ensemble of autoencoders for online network intrusion detection," Network and Distributed Systems Security Symposium (NDSS), 2018. Sun, "Acquisitional rule-based engine for discovering internet-of-things devices," in USENIX Security Symposium, 2018, pp. Feamster, "Enhancing transparency: Internet video quality inference from network traffic," Research Conference on Communications, Information and Internet Policy, 2018. De Donato, "BISmark: A Testbed for Deploying Measurements and Applications in Broadband Access Networks," in USENIX Annual Technical Conference (ATC), 2014. Feamster, "Cleartext Data Transmissions in Consumer IoT Medical Devices," in Proceedings of the 2017 Workshop on Internet of Things Security and Privacy. Feamster, "Keeping the smart home private with smart (er) iot traffic shaping," arXiv preprint arXiv:1812.00955, 2018. Durumeric, "All Things Considered: An Analysis of IoT Devices on Home Networks," in USENIX Security Symposium, 2019. (2017) Gartner Says 8.4 Billion Connected "Things" Will Be in Use in 2017, Up 31 Percent From 2016. Available: Google Scholar Digital Library Le, "Devicemien: Network device behavior modeling for identifying unknown iot devices," in Proceedings of the International Conference on Internet of Things Design and Implementation, ser. Feamster, "Security and Privacy Analyses of Internet of Things Children's Toys," IEEE Internet of Things Journal, vol. Zhou, "Understanding the Mirai Botnet," in USENIX Security Symposium, 2017. To facilitate future reproducible research in smart homes, we will release the IoT Inspector data to the public. Finally, we find widespread cross-border communications, sometimes unencrypted, between devices and Internet services that are located in countries with potentially poor privacy practices. Second, we discover that smart TVs from at least 10 vendors communicated with advertising and tracking services. ![]() First, we find that many device vendors, including Amazon and Google, use outdated TLS versions and send unencrypted traffic, sometimes to advertising and tracking services. We demonstrate how this data enables new research into smart homes through two case studies focused on security and privacy. At the time of publication, IoT Inspector is still gaining users and collecting data from more devices. Between Apand January 21, 2020, 5,404 users have installed IoT Inspector, allowing us to collect labeled network traffic from 54,094 smart home devices. ![]() 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. 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. ![]() 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. The proliferation of smart home devices has created new opportunities for empirical research in ubiquitous computing, ranging from security and privacy to personal health. ![]()
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