Keystroke recognition using chaotic neural network

Abstract

Keystroke dynamics, which distinguishes individual by its typing rhythm, is the most prevalent behavior biometrie authentication system. Neural Network is the active research area where different area has been presented. This paper present a keystroke dynamics Biometric system using chaotic neural network as the dimensional reduction and pattern recognition of the individual. Biometric scheme are being extensively used as their security qualities over the prior authentication system based on their history, that is the records were easily lost, guessed or forget. Biometric is more complex than password and is unique for each individual. In this work, the focus is made on the dwell time and flight time of the users' typing to recognize or reject an imposter. For this paper, the recognition rate obtained for the application of chaotic neural network was 99.1%.