Learning User Keystroke Patterns for Authentication

  • Zhao Y
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Abstract

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 K-star. 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 3-Grams is more accurate than 2-Grams and
4-Grams for feature extraction. Also, combination of attributes tend to
result higher accuracy.

Author-supplied keywords

  • bayesian
  • chine learning
  • decision tree
  • instance-based learning
  • keystroke authentication
  • ma-
  • pattern recognition

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Authors

  • Ying Zhao

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