Personal Authentication Based on Keystroke Dynamics Using Soft Computing Techniques

The need to secure sensitive data and computer systems from intruders, while allowing ease of access for authenticate user 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 promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In the experiment, we measure mean, standard deviation and median values of keystroke features such as latency, duration, digraph and their combinations and compare their performance. Particle swarm optimization (PSO), genetic algorithm (GA) and the proposed ant colony optimization (ACO) are used for feature subset selection. Back propagation neural network (BPNN) is used for classification. ACO gives better performance than PSO and GA with regard to feature reduction rate and classification accuracy. Using digraph as the feature for feature subset selection is novel and show good classification performance.