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"Your Eyes Tell You Have Used This Password Before" : Identifying Password Reuse from Gaze and Keystroke Dynamics

Title data

Abdrabou, Yasmeen ; Schütte, Johannes ; Shams, Ahmed ; Pfeuffer, Ken ; Buschek, Daniel ; Khamis, Mohamed ; Alt, Florian:
"Your Eyes Tell You Have Used This Password Before" : Identifying Password Reuse from Gaze and Keystroke Dynamics.
In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. - New York, NY : Association for Computing Machinery , 2022
ISBN 978-1-4503-9157-3
DOI: https://doi.org/10.1145/3491102.3517531

Official URL: Volltext

Project information

Project title:
Project's official titleProject's id
AI Tools - Continuous Interaction with Computational Intelligence ToolsNo information

Abstract in another language

A significant drawback of text passwords for end-user authentication is password reuse. We propose a novel approach to detect password reuse by leveraging gaze as well as typing behavior and study its accuracy. We collected gaze and typing behavior from 49 users while creating accounts for 1) a webmail client and 2) a news website. While most participants came up with a new password, 32 reported having reused an old password when setting up their accounts. We then compared different ML models to detect password reuse from the collected data. Our models achieve an accuracy of up to 87.7 in detecting password reuse from gaze, 75.8 accuracy from typing, and 88.75 when considering both types of behavior. We demonstrate that using gaze, password reuse can already be detected during the registration process, before users entered their password. Our work paves the road for developing novel interventions to prevent password reuse.

Further data

Item Type: Article in a book
Refereed: Yes
Keywords: Keystroke Dynamics; Machine Learning; Passwords; Gaze Behavior
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science
Result of work at the UBT: Yes
DDC Subjects: 000 Computer Science, information, general works > 004 Computer science
Date Deposited: 15 Jun 2022 09:25
Last Modified: 15 Jun 2022 09:25
URI: https://eref.uni-bayreuth.de/id/eprint/70106