Identity authentication based on keystroke dynamics using genetic algorithm and particle Swarm Optimization

Techniques based on biometrics have been successfully applied to personal identification systems. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In this work, mean and standard deviation of latency, duration and digraph is measured as keystroke features. Optimization techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) are used for feature subset selection and their performance is compared. Particle swarm optimization gave moderate performance than genetic algorithm. Using the duration as the feature for feature subset selection is novel.