Feature subset selection in keystroke dynamics using ant colony optimization

Abstract

The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticated users is one of the main problems in computer security. Traditionally, passwords have been the usual method for controlling access to computer systems but this approach has many inherent flaws. Keystroke Dynamics is a relatively new method of biometric identification and provides a comparatively inexpensive and low profile method of hardening the normal login and password process. This paper presents the feature subset selection in Keystroke Dynamics for identity verification, and it reports the results of experimenting Ant Colony Optimization technique on keystroke duration, latency and digraph for feature subset selection. Here, the Ant Colony Optimization is used to reduce the redundant feature values and minimize the search space. Optimum feature subset is obtained using keystroke duration values when compared with the other two feature values.   Key words: Feature extraction, feature subset selection, mean and standarddeviation, ant colony optimization algorithm, keystroke dynamics.