Performance of keystroke biometrics authentication system using artificial neural network (ANN) and distance classifier method

Having a secure information system depends on successful authentication of legitimate users so as to prevent attacks from fraudulent persons. Traditional information security systems use a password or personal identification number (PIN). This means they can be easily accessed by unauthorized persons without access being noticed. 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. The results are compared with a Radial Basis Function (RBF) neural network and several distance classifier method used in literature based on Equal Error Rate (EER).