Presentation 2011-03-04
User Authentication Using Neural Network Based on Keystroke Characteristics
Keishi SAKURAI, Takao MIYAMOTO, Shigeki AOKI, Motoi IWATA, Akira SHIOZAKI,
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Abstract(in English) Recently, several user authentication methods using keystroke characteristics for free-style text have been presented. In this paper, we propose a new user authentication method using the measures based on keystroke characteristics. Firstly, we set the parameters of each measure for each user by using templates of the user and others collected in advance. Secondly, we set the weight coefficients of a neural network for each user using the templates. Finally, we calculate the similarities between the user and his/her template, and authenticate the user by inputting the similarities to the neural network. We have performed experiments by this method and have achieved 0.17% of false acceptance rate and 2.38% of false rejection rate.
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Keyword(in English) Keystroke / Neural network / User authentication
Paper # IT2010-101,ISEC2010-105,WBS2010-80
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Committee ISEC
Conference Date 2011/2/24(1days)
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Registration To Information Security (ISEC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) User Authentication Using Neural Network Based on Keystroke Characteristics
Sub Title (in English)
Keyword(1) Keystroke
Keyword(2) Neural network
Keyword(3) User authentication
1st Author's Name Keishi SAKURAI
1st Author's Affiliation Graduate School of Engineering, Osaka Prefecture University()
2nd Author's Name Takao MIYAMOTO
2nd Author's Affiliation Faculty of Liberal Arts and Science, Osaka Prefecture University
3rd Author's Name Shigeki AOKI
3rd Author's Affiliation Faculty of Liberal Arts and Science, Osaka Prefecture University
4th Author's Name Motoi IWATA
4th Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
5th Author's Name Akira SHIOZAKI
5th Author's Affiliation Graduate School of Engineering, Osaka Prefecture University
Date 2011-03-04
Paper # IT2010-101,ISEC2010-105,WBS2010-80
Volume (vol) vol.110
Number (no) 443
Page pp.pp.-
#Pages 8
Date of Issue