A comparison of artificial neural networks and cluster analysis for typing biometrics authentication

Password authentication is the most commonly used identification system in today´s computer world. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. Our research focuses on using the time period between keystrokes as the measure of the individual´s typing pattern. The typing pattern of a particular individual can be represented by the weights of a fully trained multilayer perceptron. Alternatively, each user´s typing pattern can be viewed as a cluster of measurements that can be differentiated from clusters of other users