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Authenticating the identity of computer users with typing biometrics and the fuzzy min-max neural network
journal contribution
posted on 2009-01-01, 00:00 authored by A Quteishat, Chee Peng LimChee Peng Lim, C Loy, W LaiIn this paper, typing biometrics is applied as an additional security measure to the password-based or Personal Identification Number (PIN)-based systems to authenticate the identity of computer users. In particular, keystroke pressure and latency signals are analyzed using the Fuzzy Min-Max (FMM) neural network for authentication purposes. A special pressure-sensitive keyboard is designed to collect keystroke pressure signals, in addition to the latency signals, from computer users when they type their passwords. Based on the keystroke pressure and latency signals, the FMM network is employed to classify the computer users into two categories, i.e., genuine users or impostors. To assess the effectiveness of the proposed approach, two sets of experiments are conducted, and the results are compared with those from statistical methods and neural network models. The experimental outcomes positively demonstrate the potentials of using typing biometrics and the FMM network to provide an additional security layer for the current password-based or PIN-based methods in authenticating the identity of computer users.
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Journal
International journal of biomedical soft computing and human sciencesVolume
14Issue
1Pagination
47 - 53Publisher
Baiomedikaru Faji Shisutemu KenkyukaiLocation
Fukuoka, JapanISSN
1345-1529Language
engPublication classification
C1.1 Refereed article in a scholarly journalUsage metrics
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