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 Multi Layer Perceptron (MLP). Alternatively, each user’s typing pattern can be viewed as a cluster of measurements that can be differentiated from clusters of other users.