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GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments. /
Abdrabou, Yasmeen; Shams, Ahmed; Mantawy, Mohamed Omar et al.
Proceedings - ETRA 2021: ACM Symposium on Eye Tracking Research and Applications, Full Papers Proceedings. ed. / Stephen N. Spencer. Association for Computing Machinery (ACM), 2021. p. 1-12 0 (Eye Tracking Research and Applications Symposium (ETRA); Vol. PartF169256).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Abdrabou, Y, Shams, A, Mantawy, MO
, Khan, AA, Khamis, M, Alt, F & Abdelrahman, Y 2021,
GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments. in SN Spencer (ed.),
Proceedings - ETRA 2021: ACM Symposium on Eye Tracking Research and Applications, Full Papers Proceedings., 0, Eye Tracking Research and Applications Symposium (ETRA), vol. PartF169256, Association for Computing Machinery (ACM), pp. 1-12.
https://doi.org/10.1145/3448017.3457384
APA
Abdrabou, Y., Shams, A., Mantawy, M. O.
, Khan, A. A., Khamis, M., Alt, F., & Abdelrahman, Y. (2021).
GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments. In S. N. Spencer (Ed.),
Proceedings - ETRA 2021: ACM Symposium on Eye Tracking Research and Applications, Full Papers Proceedings (pp. 1-12). Article 0 (Eye Tracking Research and Applications Symposium (ETRA); Vol. PartF169256). Association for Computing Machinery (ACM).
https://doi.org/10.1145/3448017.3457384
Vancouver
Author
Bibtex
@inproceedings{71e8f8d14eff4165805e7dadd7ee65f6,
title = "GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments.",
abstract = "We investigate the use of gaze behaviour as a means to assess password strength as perceived by users. We contribute to the effort of making users choose passwords that are robust against guessing-attacks. Our particular idea is to consider also the users' understanding of password strength in security mechanisms. We demonstrate how eye tracking can enable this: by analysing people's gaze behaviour during password creation, its strength can be determined. To demonstrate the feasibility of this approach, we present a proof of concept study (N = 15) in which we asked participants to create weak and strong passwords. Our findings reveal that it is possible to estimate password strength from gaze behaviour with an accuracy of 86% using Machine Learning. Thus, we enable research on novel interfaces that consider users' understanding with the ultimate goal of making users choose stronger passwords.",
keywords = "Eye-tracking, Gaze Behaviour, Password Meters, Password Strength",
author = "Yasmeen Abdrabou and Ahmed Shams and Mantawy, {Mohamed Omar} and Khan, {Anam Ahmad} and Mohamed Khamis and Florian Alt and Yomna Abdelrahman",
year = "2021",
month = may,
day = "25",
doi = "10.1145/3448017.3457384",
language = "English",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery (ACM)",
pages = "1--12",
editor = "Spencer, {Stephen N.}",
booktitle = "Proceedings - ETRA 2021",
address = "United States",
}
RIS
TY - GEN
T1 - GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments.
AU - Abdrabou, Yasmeen
AU - Shams, Ahmed
AU - Mantawy, Mohamed Omar
AU - Khan, Anam Ahmad
AU - Khamis, Mohamed
AU - Alt, Florian
AU - Abdelrahman, Yomna
PY - 2021/5/25
Y1 - 2021/5/25
N2 - We investigate the use of gaze behaviour as a means to assess password strength as perceived by users. We contribute to the effort of making users choose passwords that are robust against guessing-attacks. Our particular idea is to consider also the users' understanding of password strength in security mechanisms. We demonstrate how eye tracking can enable this: by analysing people's gaze behaviour during password creation, its strength can be determined. To demonstrate the feasibility of this approach, we present a proof of concept study (N = 15) in which we asked participants to create weak and strong passwords. Our findings reveal that it is possible to estimate password strength from gaze behaviour with an accuracy of 86% using Machine Learning. Thus, we enable research on novel interfaces that consider users' understanding with the ultimate goal of making users choose stronger passwords.
AB - We investigate the use of gaze behaviour as a means to assess password strength as perceived by users. We contribute to the effort of making users choose passwords that are robust against guessing-attacks. Our particular idea is to consider also the users' understanding of password strength in security mechanisms. We demonstrate how eye tracking can enable this: by analysing people's gaze behaviour during password creation, its strength can be determined. To demonstrate the feasibility of this approach, we present a proof of concept study (N = 15) in which we asked participants to create weak and strong passwords. Our findings reveal that it is possible to estimate password strength from gaze behaviour with an accuracy of 86% using Machine Learning. Thus, we enable research on novel interfaces that consider users' understanding with the ultimate goal of making users choose stronger passwords.
KW - Eye-tracking
KW - Gaze Behaviour
KW - Password Meters
KW - Password Strength
U2 - 10.1145/3448017.3457384
DO - 10.1145/3448017.3457384
M3 - Conference contribution/Paper
T3 - Eye Tracking Research and Applications Symposium (ETRA)
SP - 1
EP - 12
BT - Proceedings - ETRA 2021
A2 - Spencer, Stephen N.
PB - Association for Computing Machinery (ACM)
ER -
