Feature subset selection using PSO-ELM-ANP wrapper approach for keystroke dynamics

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. © 2013 Springer.