On Accuracy of Classification-Based Keystroke Dynamics for Continuous User Authentication

Alaa Darabseh, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The aim of this research is to advance the user active authentication using keystroke dynamics. Through this research, we assess the performance and influence of various keystroke features on keystroke dynamics authentication systems. In particular, we investigate the performance of keystroke features on a subset of most frequently used English words. The performance of four features such as i) key duration, ii) flight time latency, iii) diagraph time latency, and iv) word total time duration are analyzed. Two machine learning techniques are employed for assessing keystroke authentications. The selected classification methods are support vector machine (SVM), and k-nearest neighbor classifier (K-NN). The logged experimental data are captured for 28 users. The experimental results show that key duration time offers the best performance result among all four keystroke features, followed by word total time.

Original languageEnglish
Title of host publicationProceedings - 2015 International Conference on Cyberworlds, CW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages321-324
Number of pages4
ISBN (Electronic)9781467394031
DOIs
StatePublished - Feb 3 2016
EventInternational Conference on Cyberworlds, CW 2015 - Visby, Sweden
Duration: Oct 7 2015Oct 9 2015

Publication series

NameProceedings - 2015 International Conference on Cyberworlds, CW 2015

Conference

ConferenceInternational Conference on Cyberworlds, CW 2015
CountrySweden
CityVisby
Period10/7/1510/9/15

Keywords

  • Authentication
  • Keystroke Dynamics
  • Performance
  • Security

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