Improving the Performance of Free-text Keystroke Dynamics Authentication by Fusion

Arwa Alsultan, Kevin Warwick, Hong Wei

Research output: Contribution to journalArticle

2 Citations (Scopus)
19 Downloads (Pure)

Abstract

Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR).

Original languageEnglish
Pages (from-to)1024-1033
JournalApplied Soft Computing
Volume70
Early online date21 Nov 2017
DOIs
Publication statusPublished - Sep 2018

Fingerprint

Authentication
Fusion reactions
Ant colony optimization
Decision trees
Support vector machines
Feature extraction
Classifiers

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, [(in press), (2017)] DOI: 10.1016/j.asoc.2017.11.018

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Free-text keystroke dynamics authentication
  • feature-level fusion
  • decision-level fusion
  • SVMs
  • ACO
  • decision tree

Cite this

Improving the Performance of Free-text Keystroke Dynamics Authentication by Fusion. / Alsultan, Arwa; Warwick, Kevin; Wei, Hong.

In: Applied Soft Computing, Vol. 70, 09.2018, p. 1024-1033.

Research output: Contribution to journalArticle

@article{c21764e84f8f471caf9d331a064469df,
title = "Improving the Performance of Free-text Keystroke Dynamics Authentication by Fusion",
abstract = "Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00{\%} False Accept Rate (FAR) and 0.00{\%} False Reject Rate (FRR).",
keywords = "Free-text keystroke dynamics authentication, feature-level fusion, decision-level fusion, SVMs, ACO, decision tree",
author = "Arwa Alsultan and Kevin Warwick and Hong Wei",
note = "NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, [(in press), (2017)] DOI: 10.1016/j.asoc.2017.11.018 {\circledC} 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/",
year = "2018",
month = "9",
doi = "10.1016/j.asoc.2017.11.018",
language = "English",
volume = "70",
pages = "1024--1033",
journal = "Applied Soft Computing",
issn = "1568-4946",
publisher = "Elsevier",

}

TY - JOUR

T1 - Improving the Performance of Free-text Keystroke Dynamics Authentication by Fusion

AU - Alsultan, Arwa

AU - Warwick, Kevin

AU - Wei, Hong

N1 - NOTICE: this is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, [(in press), (2017)] DOI: 10.1016/j.asoc.2017.11.018 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

PY - 2018/9

Y1 - 2018/9

N2 - Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR).

AB - Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR).

KW - Free-text keystroke dynamics authentication

KW - feature-level fusion

KW - decision-level fusion

KW - SVMs

KW - ACO

KW - decision tree

U2 - 10.1016/j.asoc.2017.11.018

DO - 10.1016/j.asoc.2017.11.018

M3 - Article

VL - 70

SP - 1024

EP - 1033

JO - Applied Soft Computing

JF - Applied Soft Computing

SN - 1568-4946

ER -