research-article

Next2You: Robust Copresence Detection Based on Channel State Information

Authors:

Mikhail Fomichev,

Luis F. Abanto-leon,

Alejandro Molina,

Matthias HollickAuthors Info & Claims

ACM Transactions on Internet of Things, Volume 3, Issue 2

Article No.: 11, Pages 1 - 31

https://doi.org/10.1145/3491244

Published: 15 February 2022 Publication History

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.

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Cited By

Alhoraibi LAlghazzawi DAlhebshi RNawaf LCarroll F(2024)Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency ResponseIET Signal Processing10.1049/2024/88846882024(1-13)Online publication date: 28-Mar-2024
https://doi.org/10.1049/2024/8884688
Shrestha PTruong HToivonen PSaxena NTarkoma SNurmi P(2023)Chirp-LocPervasive and Mobile Computing10.1016/j.pmcj.2022.10172088:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2022.101720
Martin-Escalona IZola E(2022)Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT ObservablesSensors10.3390/s2301026723:1(267)Online publication date: 27-Dec-2022
https://doi.org/10.3390/s23010267

Index Terms

Next2You: Robust Copresence Detection Based on Channel State Information
1. Computer systems organization
  1. Embedded and cyber-physical systems
    1. Sensor networks
2. Security and privacy
  1. Security services
    1. Authentication

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cover image ACM Transactions on Internet of Things

ACM Transactions on Internet of Things Volume 3, Issue 2

May 2022

214 pages

EISSN:2577-6207

DOI:10.1145/3505220

Editors:
Schahram Dustdar
TU Wien, Austria
,
Gian Pietro Picco
University of Trento, Italy

Issue’s Table of Contents

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 15 February 2022

Accepted: 01 October 2021

Revised: 01 September 2021

Received: 01 February 2021

Published in TIOT Volume 3, Issue 2

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Research Council of Norway
German Research Foundation (DFG)
Collaborative Research Center (CRC)
German Federal Ministry of Education and Research
Hessian Ministry of Higher Education
National Research Center for Applied Cybersecurity ATHENE

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Cited By

Alhoraibi LAlghazzawi DAlhebshi RNawaf LCarroll F(2024)Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency ResponseIET Signal Processing10.1049/2024/88846882024(1-13)Online publication date: 28-Mar-2024
https://doi.org/10.1049/2024/8884688
Shrestha PTruong HToivonen PSaxena NTarkoma SNurmi P(2023)Chirp-LocPervasive and Mobile Computing10.1016/j.pmcj.2022.10172088:COnline publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1016/j.pmcj.2022.101720
Martin-Escalona IZola E(2022)Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT ObservablesSensors10.3390/s2301026723:1(267)Online publication date: 27-Dec-2022
https://doi.org/10.3390/s23010267

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