@InProceedings{abdrabou2021etra,
  author    = {Abdrabou, Yasmeen and Shams, Ahmed and Mantawy, Mohamed Omar and Ahmad Khan, Anam and Khamis, Mohamed and Alt, Florian and Abdelrahman, Yomna},
  booktitle = {{Proceedings of the 2021 ACM Symposium on Eye Tracking Research \& Applications}},
  title     = {{GazeMeter: Exploring the Usage of Gaze Behaviour to Enhance Password Assessments}},
  year      = {2021},
  address   = {New York, NY, USA},
  note      = {abdrabou2021etra},
  publisher = {Association for Computing Machinery},
  series    = {ETRA '21},
  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.},
  articleno = {9},
  doi       = {10.1145/3448017.3457384},
  isbn      = {9781450383448},
  numpages  = {12},
  owner     = {florian},
  timestamp = {2021.06.10},
  url       = {http://www.florian-alt.org/unibw/wp-content/publications/abdrabou2020etra.pdf},
  video     = {abdrabou2021etra},
}