Fuzzy and Markov Models for Keystroke Biometrics Authentication

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

Keystroke biometrics authentication system is based on a password and keystroke biometric features captured when a user is typing in the password. The system offers a higher level of security and convenience for computers. The system does not require additional hardware as it can be used with any existing keyboard, making it relatively inexpensive and fairly unobtrusive to the user. There have been existing research publications on keystroke biometrics authentication that have solved problems in selecting appropriate keystroke features and modeling users. However methods for calculating score to reduce authentication error are not taken into account. Therefore we propose to use Markov modeling and fuzzy set theory-based normalization methods for keystroke biometrics authentication that can reduce both false rejection and false acceptance rates. Experiments showed better performance for the proposed methods
Original languageEnglish
Title of host publicationNew Advances in Simulation, Modeling and Optimization
EditorsD Xu
Place of PublicationBeijing, China
PublisherWorld Scientific Publishing
Pages89-94
Number of pages6
ISBN (Electronic)9879606766053
ISBN (Print)9789606766077
Publication statusPublished - 2007
Event7th WSEAS International Conference on Signal, Speech and Image Processing - Beijing, China
Duration: 15 Sep 200717 Sep 2007

Conference

Conference7th WSEAS International Conference on Signal, Speech and Image Processing
CountryChina
CityBeijing
Period15/09/0717/09/07

Fingerprint

Biometrics
Authentication
Fuzzy set theory
Computer hardware
Computer systems
Experiments

Cite this

Tran, D., Ma, W., Chetty, G., & Sharma, D. (2007). Fuzzy and Markov Models for Keystroke Biometrics Authentication. In D. Xu (Ed.), New Advances in Simulation, Modeling and Optimization (pp. 89-94). Beijing, China: World Scientific Publishing.
Tran, Dat ; Ma, Wanli ; Chetty, Girija ; Sharma, Dharmendra. / Fuzzy and Markov Models for Keystroke Biometrics Authentication. New Advances in Simulation, Modeling and Optimization. editor / D Xu. Beijing, China : World Scientific Publishing, 2007. pp. 89-94
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abstract = "Keystroke biometrics authentication system is based on a password and keystroke biometric features captured when a user is typing in the password. The system offers a higher level of security and convenience for computers. The system does not require additional hardware as it can be used with any existing keyboard, making it relatively inexpensive and fairly unobtrusive to the user. There have been existing research publications on keystroke biometrics authentication that have solved problems in selecting appropriate keystroke features and modeling users. However methods for calculating score to reduce authentication error are not taken into account. Therefore we propose to use Markov modeling and fuzzy set theory-based normalization methods for keystroke biometrics authentication that can reduce both false rejection and false acceptance rates. Experiments showed better performance for the proposed methods",
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Tran, D, Ma, W, Chetty, G & Sharma, D 2007, Fuzzy and Markov Models for Keystroke Biometrics Authentication. in D Xu (ed.), New Advances in Simulation, Modeling and Optimization. World Scientific Publishing, Beijing, China, pp. 89-94, 7th WSEAS International Conference on Signal, Speech and Image Processing, Beijing, China, 15/09/07.

Fuzzy and Markov Models for Keystroke Biometrics Authentication. / Tran, Dat; Ma, Wanli; Chetty, Girija; Sharma, Dharmendra.

New Advances in Simulation, Modeling and Optimization. ed. / D Xu. Beijing, China : World Scientific Publishing, 2007. p. 89-94.

Research output: A Conference proceeding or a Chapter in BookConference contribution

TY - GEN

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AU - Sharma, Dharmendra

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AB - Keystroke biometrics authentication system is based on a password and keystroke biometric features captured when a user is typing in the password. The system offers a higher level of security and convenience for computers. The system does not require additional hardware as it can be used with any existing keyboard, making it relatively inexpensive and fairly unobtrusive to the user. There have been existing research publications on keystroke biometrics authentication that have solved problems in selecting appropriate keystroke features and modeling users. However methods for calculating score to reduce authentication error are not taken into account. Therefore we propose to use Markov modeling and fuzzy set theory-based normalization methods for keystroke biometrics authentication that can reduce both false rejection and false acceptance rates. Experiments showed better performance for the proposed methods

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BT - New Advances in Simulation, Modeling and Optimization

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Tran D, Ma W, Chetty G, Sharma D. Fuzzy and Markov Models for Keystroke Biometrics Authentication. In Xu D, editor, New Advances in Simulation, Modeling and Optimization. Beijing, China: World Scientific Publishing. 2007. p. 89-94