Effective user authentications using keystroke dynamics based on feature selections

Alaa Darabseh, Akbar Siami Namin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Efficient keystroke authentication systems should have the ability to capture and build the user's pattern in minimal time. These systems also should be able to achieve quickest detection while maintaining good detection accuracy. However, maintaining high detection accuracy and minimal detection delay are conflicting requirements that need to be balanced. A possible approach to tackle this problem is reducing the number of features that need to be learned by a classifier and thereby decreasing the processing time. A wrapper based feature subset selection approach is presented in this paper with the objective of reducing the dimensionality of the user data through identifying a smaller subset of features that represent the most discriminating features in keystrokes dynamic. Several features selection techniques such as genetic and greedy algorithms, best first search Algorithms, and Particle Swarm Optimization (PSO) are used to search for the best subset features. These selection techniques are integrated (Wrapped) with different machine learning classifiers namely Support Vector Machine (SVM), Naive Bayesian (NB), and K Nearest Neighbors (KNN) for feature subset selection procedure that can automatically select the most appropriate and representative subset of features.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages307-312
Number of pages6
ISBN (Electronic)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Publication series

NameProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015

Conference

ConferenceIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
CountryUnited States
CityMiami
Period12/9/1512/11/15

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Keywords

  • Authentication
  • Biometrics
  • Keystroke dynamics
  • Keystroke feature
  • Security

Cite this

Darabseh, A., & Namin, A. S. (2016). Effective user authentications using keystroke dynamics based on feature selections. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (pp. 307-312). [7424326] (Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2015.90