An Efficient Feature Selection Technique for User Authentication using Keystroke Dynamics
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Date
2011
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Abstract
Securing the sensitive data and computer systems by allowing
ease access to authenticated users and withstanding the attacks of
imposters is one of the major challenges in the field of computer
security. ID and password are the most widely used method for
authenticating the computer systems. But, this method has many
loop holes such as password sharing, shoulder surfing, brute
force attack, dictionary dttack, guessing, phishing and many
more. Keystroke Dynamics is one of the famous and inexpensive
behavioral biometric technologies, which identifies the
aulhenticity of a user when the user is working via a keyboard.
Keystroke features like dwell time, flight time, di-graph, trigraph
and virtual key force of every user are used in this paper.
For the purpose of preprocessing Z-Score method is used. Ant
Colony Optimization (ACO), Particle Swarm Optimization
(PSO), Genetic Algorithm (GA) algorithm is used with Extreme
Learning Machine (ELM) for feature subset selection. In order to
classify the obtained results ELM algorithm is used. Comparison
of ACO, PSO and GA with ELM respectively is done to find the
best method for feature subset selection. From the results, it is
revealed that ACO with ELM is best for feature subset selection.