Keystroke Dynamics Authentication System Using Neural Network

The fact that computers regularly store private, sensitive and classified information makes it very important that we can confidently identify their users. Traditionally, this has been achieved through password authentication systems. However, these systems are far from perfect. For instance, if a password becomes compromised, it is no longer adequate for authenticating its rightful owner. In the hope of improving on this, there exists ongoing research into utilising the idiosyncrasies of a user’s interaction with a computer as a form of authentication. So far in this field the most promising techniques focus on patterns in the timing of a user’s typing. We shall refer to this as ‘biometric keystroke authentication’. This paper focuses on the time interval between keystrokes as a feature of individual’s typing patterns to recognize authentic users and reject imposters. A Multilayer Perceptron (MLP) neural network is used to train and validate the features. The classifier is used to analyse the features of the user. Authentication of a user is accomplished using a classifier and appropriate adaptation of the user sample is introduced upon successive authentication.