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.