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Internet Things"],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in the<jats:bold>Internet of Things (<\/jats:bold><jats:sc><jats:bold>IoT<\/jats:bold><\/jats:sc><jats:bold>)<\/jats:bold>. Such schemes allow one device to verify proximity of another device without user assistance utilizing their physical context (e.g., audio). The state-of-the-art copresence detection schemes suffer from two major limitations: (1) They cannot accurately detect copresence in low-entropy context (e.g., empty room with few events occurring) and insufficiently separated environments (e.g., adjacent rooms), (2) They require devices to have common sensors (e.g., microphones) to capture context, making them impractical on devices with heterogeneous sensors. We address these limitations, proposing<jats:italic>Next2You<\/jats:italic>, a novel copresence detection scheme utilizing channel state information (CSI). In particular, we leverage magnitude and phase values from a range of subcarriers specifying a Wi-Fi channel to capture a robust wireless context created when devices communicate. We implement<jats:italic>Next2You<\/jats:italic>on off-the-shelf smartphones relying only on ubiquitous Wi-Fi chipsets and evaluate it based on over 95 hours of CSI measurements that we collect in five real-world scenarios.<jats:italic>Next2You<\/jats:italic>achieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability of<jats:italic>Next2You<\/jats:italic>to work reliably in real-time and its robustness to various attacks.<\/jats:p>","DOI":"10.1145\/3491244","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T11:43:11Z","timestamp":1644925391000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Next2You: Robust Copresence Detection Based on Channel State Information"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9697-0359","authenticated-orcid":false,"given":"Mikhail","family":"Fomichev","sequence":"first","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-7928","authenticated-orcid":false,"given":"Luis F.","family":"Abanto-leon","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8420-7217","authenticated-orcid":false,"given":"Max","family":"Stiegler","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4509-9174","authenticated-orcid":false,"given":"Alejandro","family":"Molina","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1068-6053","authenticated-orcid":false,"given":"Jakob","family":"Link","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9163-5989","authenticated-orcid":false,"given":"Matthias","family":"Hollick","sequence":"additional","affiliation":[{"name":"Technical University of Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2016.2586844"},{"key":"e_1_3_3_3_2","article-title":"Deep neural networks meet CSI-based authentication","author":"Abyaneh Amirhossein Yazdani","year":"2018","unstructured":"Amirhossein Yazdani Abyaneh, Ali Hosein Gharari Foumani, and Vahid Pourahmadi. 2018. 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