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Next2You: Robust Copresence Detection Based on Channel State Information

Fomichev, Mikhail ; Abanto-Leon, Luis F. ; Stiegler, Max ; Molina Ramirez, Alejandro ; Link, Jakob ; Hollick, Matthias (2022)
Next2You: Robust Copresence Detection Based on Channel State Information.
In: ACM Transactions on Internet of Things, 3 (2)
doi: 10.1145/3491244
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in the Internet of Things (IoT). 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 Next2You, 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 Next2You 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. Next2You achieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability of Next2You to work reliably in real-time and its robustness to various attacks.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Fomichev, Mikhail ; Abanto-Leon, Luis F. ; Stiegler, Max ; Molina Ramirez, Alejandro ; Link, Jakob ; Hollick, Matthias
Art des Eintrags: Bibliographie
Titel: Next2You: Robust Copresence Detection Based on Channel State Information
Sprache: Englisch
Publikationsjahr: Mai 2022
Verlag: ACM
Titel der Zeitschrift, Zeitung oder Schriftenreihe: ACM Transactions on Internet of Things
Jahrgang/Volume einer Zeitschrift: 3
(Heft-)Nummer: 2
DOI: 10.1145/3491244
URL / URN: https://dl.acm.org/doi/10.1145/3491244
Kurzbeschreibung (Abstract):

Context-based copresence detection schemes are a necessary prerequisite to building secure and usable authentication systems in the Internet of Things (IoT). 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 Next2You, 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 Next2You 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. Next2You achieves error rates below 4%, maintaining accurate copresence detection both in low-entropy context and insufficiently separated environments. We also demonstrate the capability of Next2You to work reliably in real-time and its robustness to various attacks.

Zusätzliche Informationen:

Art.No.: 11

Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Sichere Mobile Netze
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > A: Konstruktionsmethodik > Teilprojekt A3: Migration
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > T: Transferprojekte
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > T: Transferprojekte > Transferprojekt T1: Adaptivität im Mobilfunk
Hinterlegungsdatum: 24 Mai 2022 07:13
Letzte Änderung: 16 Aug 2022 07:59
PPN:
Projekte: German Research Foundation (DFG) - Collaborative Research Center (CRC) 1053 MAKI A3, German Research Foundation (DFG) - Collaborative Research Center (CRC) 1053 MAKI B5G-Cell, Research Council of Norway - Parrot (311197), National Research Center for Applied Cybersecurity ATHENE
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