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Behavio2Auth: Sensor-based Behavior Biometric Authentication for Smartphones

Published: 07 March 2019 Publication History

Abstract

Many mobile applications use mobile built-in sensors to provide users with a plethora of services to collect and store a mass of sensitive data. These mobile devices need to be protected from unauthorized access and allow access for legitimate users only. In this paper, the problem of unauthorized access is addressed by identifying the user during activities under two considered scenarios: walking and sitting.We authenticate users continuously and implicitly based on micro-movements by leveraging the typing activity information on the screen. These micro-movements come from the user's typing on the touchscreen while walking or sitting. Accelerometer data were analyzed to capture these micro-movements and build the proposed authentication model, named Behavio2Auth. Assuming that each individual has a distinct movement pattern, this hypothesis is used to differentiate between users. A set of experiments were conducted on 100 participants which show that Behavio2Auth is efficient for recognizing smartphone users during walking and sitting scenarios and achieves low Equal Error Rate of only (10%, 16%) with an Area Under the Curve of (95%, 91%).

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Cited By

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  • (2025)TBAuth: A continuous authentication framework based on tap behavior for smartphonesExpert Systems with Applications10.1016/j.eswa.2024.125811264(125811)Online publication date: Mar-2025
  • (2024)AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality DataIEEE Internet of Things Journal10.1109/JIOT.2023.331471711:4(6928-6942)Online publication date: 15-Feb-2024
  • (2024)Sensor-based authentication in smartphone: A systematic reviewJournal of Engineering Research10.1016/j.jer.2024.02.003Online publication date: Feb-2024
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cover image ACM Other conferences
ArabWIC 2019: Proceedings of the ArabWIC 6th Annual International Conference Research Track
March 2019
136 pages
ISBN:9781450360890
DOI:10.1145/3333165
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Google Inc.
  • Microsoft: Microsoft
  • Facebook: Facebook
  • ORACLE: ORACLE
  • IBM: IBM

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2019

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Author Tags

  1. Authentication
  2. Biometrics
  3. Motion Sensors
  4. Smartphone

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  • Research-article
  • Research
  • Refereed limited

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ArabWIC 2019

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ArabWIC 2019 Paper Acceptance Rate 20 of 36 submissions, 56%;
Overall Acceptance Rate 20 of 36 submissions, 56%

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Cited By

View all
  • (2025)TBAuth: A continuous authentication framework based on tap behavior for smartphonesExpert Systems with Applications10.1016/j.eswa.2024.125811264(125811)Online publication date: Mar-2025
  • (2024)AttAuth: An Implicit Authentication Framework for Smartphone Users Using Multimodality DataIEEE Internet of Things Journal10.1109/JIOT.2023.331471711:4(6928-6942)Online publication date: 15-Feb-2024
  • (2024)Sensor-based authentication in smartphone: A systematic reviewJournal of Engineering Research10.1016/j.jer.2024.02.003Online publication date: Feb-2024
  • (2022)Exploration of Machine Learning Classification Models Used for Behavioral Biometrics AuthenticationProceedings of the 2022 8th International Conference on Computer Technology Applications10.1145/3543712.3543732(176-182)Online publication date: 12-May-2022
  • (2022)AuthenticTap: A Behavioral Biometric Tap-Based User Authentication Method for Mobile Application2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)10.1109/ICITISEE57756.2022.10057646(143-148)Online publication date: 13-Dec-2022
  • (2022)A Systematic Literature Review on Latest Keystroke Dynamics Based ModelsIEEE Access10.1109/ACCESS.2022.319775610(92192-92236)Online publication date: 2022
  • (2022)Touch-based continuous mobile device authenticationJournal of Network and Computer Applications10.1016/j.jnca.2021.103162191:COnline publication date: 22-Apr-2022
  • (2021)User Authentication Schemes Using Machine Learning Methods—A ReviewProceedings of International Conference on Communication and Computational Technologies10.1007/978-981-16-3246-4_54(703-723)Online publication date: 24-Aug-2021

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