Abstract
This paper uses the keystroke dynamics in user authentication. The relationship between the distance metrics and the data template, for the first time, was analyzed and new distance based algorithm for keystroke dynamics classification was proposed. The results of the experiments on the CMU keystroke dynamics benchmark dataset 1 were evaluated with an equal error rate of 0.0614. The classifiers using the proposed distance metric outperform existing top performing keystroke dynamics classifiers which use traditional distance metrics.
Sign-in/Register to access full text options 
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.
