Feature Subset Selection using PSO-ELM-ANP Wrapper Approach for Keystroke Dynamics
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Date
2012
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Abstract
The security of computer access is important today because of huge
transactions being carried out every day via the Internet. Username with password is
the commonly used authentication mechanism. Most of the text based authentication
methods are vulnerable to many attacks as they depend on text and can be
strengthened more by combining password with key typing manner of the user.
Keystroke Dynamics is one of the famous and inexpensive behavioral biometric
technologies, which identifies the authenticity of a user when the user is working via
a keyboard. The paper uses a new feature Virtual Key Force along with the
commonly extracted timing features. Features are normalized using Z-Score
method. For feature subset selection, a wrapper based approach using Particle
Swarm Optimization—Extreme Learning Machine combined with Analytic
Network Process (PSO-ELM-ANP) is proposed. From the results, it is observed that
PSO-ELM-ANP selects less number of features for further processing.