Computer-access authentication with neural network based keystroke identity verification

This paper presents a novel application of neural nets to user identity authentication on computer-access security system. Keystroke latency is measured for each user and forms the patterns of keyboard dynamics. A three-layered backpropagation neural network with a flexible number of input nodes was used to discriminate valid users and impostors according to each individual's password keystroke pattern. System verification performance was improved by setting convergence criteria RMSE to a smaller threshold value during training procedure. The resulting system gave an 1.1% FAR (false alarm rate) in rejecting valid users and zero IPR (impostor pass rate) in accepting no impostors. The performance of the proposed identification method is superior to that of previous studies. A suitable network structure for this application was also discussed. Furthermore, the implementation of this approach requires no special hardware and is easy to be integrated with most computer systems.