A multilayer neural network system for computer access security

This paper presents a new multilayer neural network system to identify computer users. The input vectors were made up of the time intervals between successive keystrokes created by users while typing a known sequence of characters. Each input vector was classified into one of several classes, thereby identifying the user who typed the character sequence. Three types of networks were discussed: a multilayer feedforward network trained using the backpropagation algorithm, a sum-of-products network trained with a modification of backpropagation, and a new hybrid architecture that combines the two. A maximum classification accuracy of 97.5% was achieved using a neural network based pattern classifier. Such approach can improve computer access security