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Next2You: Robust Copresence Detection Based on Channel State Information

Fomichev, Mikhail ; Abanto-Leon, Luis F. ; Max, Stiegler ; 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), ACM, ISSN 2691-1914, e-ISSN 2577-6207,
DOI: 10.1145/3491244,
[Article]

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.

Item Type: Article
Erschienen: 2022
Creators: Fomichev, Mikhail ; Abanto-Leon, Luis F. ; Max, Stiegler ; Molina Ramirez, Alejandro ; Link, Jakob ; Hollick, Matthias
Title: Next2You: Robust Copresence Detection Based on Channel State Information
Language: English
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.

Journal or Publication Title: ACM Transactions on Internet of Things
Volume of the journal: 3
Issue Number: 2
Publisher: ACM
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Sichere Mobile Netze
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > A: Construction Methodology > Subproject A3: Migration
Date Deposited: 24 May 2022 07:13
DOI: 10.1145/3491244
URL / URN: https://dl.acm.org/doi/10.1145/3491244
Additional Information:

Art.No.: 11

PPN:
Projects: 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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