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The use of computer has increased rapidly as well as the use of internet applications such as e-commerce, online banking services, webmail, and blogs. All internet applications require a password authentication scheme to make sure only the genuine individual can login to the application. Passwords and personal identification numbers (PIN) have traditionally been used to access such applications [1, 2, 3]. However, it is easy for unauthorized persons to access these systems without detection. This paper addresses the issue of enhancing such systems using keystroke biometrics as a translucent level of user authentication. The paper focuses on using the time interval (key down-down) between keystrokes as a feature of individuals' typing patterns to recognize authentic users and reject imposters. A Multilayer Perceptron (MLP) neural network with a Back Propagation (BP) learning algorithm is used to train and validate the features.