How to improve performance of Neural Network in the hardened password mechanism

Abstract

A wide variety of systems, ubiquitous in our daily activities, require personal identification schemes that verify the identity of individual requesting their services. A non exhaustive list of such application includes secure access to buildings, computer systems, cellular phones, ATMs, crossing of national borders, boarding of planes among others. In the absence of robust schemes, these systems are vulnerable to the wiles of an impostor. Current systems are based on the three vertex of the authentication triangle which are, possession of the token, knowledge of a secret and possessing the required biometric. Due to weaknesses of the de facto password scheme, inclusion of its inherent keystroke rhythms, have been proposed and systems that implement such security measures are also on the market. This correspondence investigates possibility and ways for optimising performance of hardened password mechanism using the widely accepted Neural Network classifier. It represents continuation of a previous work in that direction.