Abstract
Keystroke production is influenced by a number of factors including linguistic context and structure. Previous studies in keystroke-based authentication have neglected to take these many of these into account. By incorporating the linguistic context under which keystrokes are produced, we are able to improve the accuracy of authentication experiments. We are able to reduce baseline EER by 36% on a dataset of 486 users, from a baseline of 0.0483 using only unigraph holds and digraph intervals to 0.0309 using linguistic context. Our EER results are further improved to 0.0232 by reducing the size of our feature-set through various methods of feature pruning. These results demonstrate the importance of context when authenticating a typist.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
