research-article

Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones

Authors:

Sumi HelalAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 1

Article No.: 33, Pages 1 - 30

https://doi.org/10.1145/3448080

Published: 30 March 2021 Publication History

Abstract

Pattern lock has been widely used in smartphones as a simple and effective authentication mechanism, which however is shown to be vulnerable to various attacks. In this paper, we design a novel authentication system for more secure pattern unlocking on smartphones. The basic idea is to utilize various behavior information of the user during pattern unlocking as additional authentication fingerprints, so that even if the pattern password is leaked to an attacker, the system remains safe and protected. To accommodate a variety of user contexts by our system, a context-aware module is proposed to distinguish any of such contexts (e.g., body postures when drawing the pattern) and use it to guide the authentication. Moreover, we design a polyline weighted strategy with overlapping based on the consistency of pattern lock, which analyzes the behavior information of the user during the unlock process in a fine-grained manner and takes an overall consideration the results of different polylines. Based on 14,850 samples collected from 77 participants, we have extensively evaluated the proposed system. The results demonstrate that it outperforms state-of-the-art implicit authentication based pattern lock approaches, and that each key module in our system is effective.

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Cited By

Mahfouz A(2024)User Authentication in the IoT and IIoT EnvironmentSmart and Agile Cybersecurity for IoT and IIoT Environments10.4018/979-8-3693-3451-5.ch008(169-194)Online publication date: 30-Jun-2024
https://doi.org/10.4018/979-8-3693-3451-5.ch008
Cao YDhekne AAmmar M(2024)SigningRing: Signature-based Authentication using Inertial Sensors on a Ring Form-factorProceedings of the Workshop on Body-Centric Computing Systems10.1145/3662009.3662019(11-16)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3662009.3662019
Zhou MZhou YSu SWang QLi QHu SYu CLi Z(2024)FingerPattern: Securing Pattern Lock via Fingerprint-Dependent Friction SoundIEEE Transactions on Mobile Computing10.1109/TMC.2023.333814823:6(7210-7224)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3338148
Show More Cited By

Index Terms

Fine-Grained and Context-Aware Behavioral Biometrics for Pattern Lock on Smartphones
1. Security and privacy
  1. Security services
    1. Authentication
      1. Multi-factor authentication

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 1

March 2021

1272 pages

EISSN:2474-9567

DOI:10.1145/3459088

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Copyright © 2021 ACM.

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Published: 30 March 2021

Published in IMWUT Volume 5, Issue 1

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Cited By

Mahfouz A(2024)User Authentication in the IoT and IIoT EnvironmentSmart and Agile Cybersecurity for IoT and IIoT Environments10.4018/979-8-3693-3451-5.ch008(169-194)Online publication date: 30-Jun-2024
https://doi.org/10.4018/979-8-3693-3451-5.ch008
Cao YDhekne AAmmar M(2024)SigningRing: Signature-based Authentication using Inertial Sensors on a Ring Form-factorProceedings of the Workshop on Body-Centric Computing Systems10.1145/3662009.3662019(11-16)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3662009.3662019
Zhou MZhou YSu SWang QLi QHu SYu CLi Z(2024)FingerPattern: Securing Pattern Lock via Fingerprint-Dependent Friction SoundIEEE Transactions on Mobile Computing10.1109/TMC.2023.333814823:6(7210-7224)Online publication date: Jun-2024
https://doi.org/10.1109/TMC.2023.3338148
Song YZhang YCai ZSu Z(2024)Touch Authentication for Sharing Context Using Within-Group Similarity StructureIEEE Internet of Things Journal10.1109/JIOT.2024.340232311:17(28281-28296)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3402323
Li GMa YWang WZhang JLuo H(2024)The Self-Detection Method of the Puppet Attack in Biometric FingerprintingIEEE Internet of Things Journal10.1109/JIOT.2024.336571411:10(18824-18838)Online publication date: 15-May-2024
https://doi.org/10.1109/JIOT.2024.3365714
Zhao CGao FShen Z(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
https://doi.org/10.1109/JIOT.2023.3314717
Yin YXie LJiang ZXiao FCao JLu S(2024)A Systematic Review of Human Activity Recognition Based on Mobile Devices: Overview, Progress and TrendsIEEE Communications Surveys & Tutorials10.1109/COMST.2024.335759126:2(890-929)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1109/COMST.2024.3357591
Mahfouz AHamdy AEldin MMahmoud T(2024)B2authPervasive and Mobile Computing10.1016/j.pmcj.2024.10188899:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101888
Mahfouz AMostafa HMahmoud TSharaf Eldin A(2024)M2auth: A multimodal behavioral biometric authentication using feature-level fusionNeural Computing and Applications10.1007/s00521-024-10403-yOnline publication date: 14-Sep-2024
https://doi.org/10.1007/s00521-024-10403-y
Yao MJin ZGao RQi PTao D(2024)Pattern unlocking guided multi‐modal continuous authentication for smartphone with multi‐branch context‐aware representation learning and auto encoderTransactions on Emerging Telecommunications Technologies10.1002/ett.490835:1Online publication date: 15-Jan-2024
https://dl.acm.org/doi/10.1002/ett.4908
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