Ying Zhao
Learning User Keystroke Patterns for Authentication
456 - 461
2008
2
2
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/9888
https://publications.waset.org/vol/14
World Academy of Science, Engineering and Technology
Keystroke authentication is a new access control system
to identify legitimate users via their typing behavior. In this paper,
machine learning techniques are adapted for keystroke authentication.
Seven learning methods are used to build models to differentiate user
keystroke patterns. The selected classification methods are Decision
Tree, Naive Bayesian, Instance Based Learning, Decision Table, One
Rule, Random Tree and Kstar. Among these methods, three of them
are studied in more details. The results show that machine learning
is a feasible alternative for keystroke authentication. Compared to
the conventional Nearest Neighbour method in the recent research,
learning methods especially Decision Tree can be more accurate. In
addition, the experiment results reveal that 3Grams is more accurate
than 2Grams and 4Grams for feature extraction. Also, combination
of attributes tend to result higher accuracy.
Open Science Index 14, 2008