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Keystroke patterns classification using the ARTMAP-FD neural network
conference contribution
posted on 2007-01-01, 00:00 authored by C Loy, W Lai, Chee Peng LimChee Peng LimThis paper presents the development of a keystroke dynamics-based user authentication system using the ARTMAP-FD neural network. The effectiveness of ARTMAPFD in classifying keystroke patterns is analyzed and compared against a number of widely used machine learning systems. The results show that ARTMAP-FD performs well against many of its counterparts in keystroke patterns classification. Apart from that, instead of using the conventional typing timing characteristics, the applicability of typing pressure to ascertaining user's identity is investigated. The experimental results show that combining both latency and pressure patterns can improve the Equal Error Rate (ERR) of the system.
History
Event
Intelligent Information Hiding and Multimedia Signal Processing. Conference (3rd : 2007 : Kaohsiung, Malaysia)Pagination
61 - 64Publisher
IEEELocation
Kaohsiung, MalaysiaPlace of publication
Piscataway, N. J.Start date
2007-11-26End date
2007-11-28ISBN-13
9780769529943ISBN-10
0769529941Language
engPublication classification
E1.1 Full written paper - refereedTitle of proceedings
IIHMSP 2007 : Proceedings of the 3rd International Conference on Intelligent Information Hiding and Multimedia Signal ProcessingUsage metrics
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No categories selectedKeywords
ARTMAP-FDFuzzy ARTMAPKeystroke dynamicsNovelty detectionTyping biometricsScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Software EngineeringComputer Science, Theory & MethodsEngineering, Electrical & ElectronicComputer ScienceEngineeringDYNAMICS IDENTITY VERIFICATION
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