Visible to the public Enhanced recognition of keystroke dynamics using Gaussian mixture models

TitleEnhanced recognition of keystroke dynamics using Gaussian mixture models
Publication TypeConference Paper
Year of Publication2015
AuthorsÇeker, H., Upadhyaya, S.
Conference NameMILCOM 2015 - 2015 IEEE Military Communications Conference
Date Publishedoct
Keywordsauthentication, biometrics, biometrics (access control), computer authentication, cryptographic protocols, digraph patterns, directed graphs, EER, enhanced recognition, equal error rate, error statistics, feature extraction, Gaussian density estimator, gaussian distribution, Gaussian mixture model, Gaussian Mixture Models, Gaussian processes, GMM, human computer interaction, image enhancement, image recognition, keystroke data, keystroke dynamics, learning (artificial intelligence), machine learning methods, mixture models, Outlier detection, pubcrawl170115, Standards
Abstract

Keystroke dynamics is a form of behavioral biometrics that can be used for continuous authentication of computer users. Many classifiers have been proposed for the analysis of acquired user patterns and verification of users at computer terminals. The underlying machine learning methods that use Gaussian density estimator for outlier detection typically assume that the digraph patterns in keystroke data are generated from a single Gaussian distribution. In this paper, we relax this assumption by allowing digraphs to fit more than one distribution via the Gaussian Mixture Model (GMM). We have conducted an experiment with a public data set collected in a controlled environment. Out of 30 users with dynamic text, we obtain 0.08% Equal Error Rate (EER) with 2 components by using GMM, while pure Gaussian yields 1.3% EER for the same data set (an improvement of EER by 93.8%). Our results show that GMM can recognize keystroke dynamics more precisely and authenticate users with higher confidence level.

DOI10.1109/MILCOM.2015.7357625
Citation Keyceker_enhanced_2015