An authentication system using keystroke dynamics

Abstract

There are various biometrics-based methods for user authentication. However, the best authentication method can be based on physiological/behavioural biometrics as capturing physiological biometrics may require use of special devices and that may not be available with many users. Keystroke dynamics is a simplified and easily achievable user authentication method when every user is available with a laptop or a personal computer. This paper presents a keystroke dynamics-based authentication system using Bayesian regularised feed-forward neural network. In order to train the model, a database is captured for recording keystroke dynamics of 20 users in four sessions each with 50 samples. Experimental results demonstrate that the Bayesian regularised neural network models provide the best results and are most suitable for this purpose. We are able to achieve an equal error rate of 0.9% which is better than the methods used in the existing literature for plain keystroke dynamics. We have given a comparative analysis of the performance of proposed system with existing methods.