Identity authentication based on keystroke latencies
Communications of the ACM
A simulation evaluation study of neural network techniques to computer user identification
Information Sciences: an International Journal
Authentication via keystroke dynamics
Proceedings of the 4th ACM conference on Computer and communications security
Keystroke dynamics as a biometric for authentication
Future Generation Computer Systems - Special issue on security on the Web
Rough set algorithms in classification problem
Rough set methods and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
User authentication through keystroke dynamics
ACM Transactions on Information and System Security (TISSEC)
Enhanced Password Authentication through Fuzzy Logic
IEEE Expert: Intelligent Systems and Their Applications
Fundamenta Informaticae
Password Secured Sites — Stepping Forward with Keystroke Dynamics
NWESP '05 Proceedings of the International Conference on Next Generation Web Services Practices
GA SVM wrapper ensemble for keystroke dynamics authentication
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
The rough set exploration system
Transactions on Rough Sets III
GREYC keystroke: a benchmark for keystroke dynamics biometric systems
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Keystroke dynamics with low constraints SVM based passphrase enrollment
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Continuous keystroke dynamics: A different perspective towards biometric evaluation
Information Security Tech. Report
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Keystroke dynamics is a behavioral biometric that is based on how a user enters their login details. In this study, a set of eight attributes were extracted during the course of entering login details. This collection of attributes was used to form a reference signature (a biometrics identification record) for subsequent authentication requests. The algorithm for the authentication step entails transforming the attributes into a discretised form based on the amino acid alphabet. A set of bioinformatics based algorithms are then used to perform the actual authentication test. In addition, the use of rough sets was employed in this study to determine if subsets of attributes were more important in the classification (authentication) than others. Lastly, the results of this study indicate that the error rate is less than 1% in the majority of the cases.