A Novel Approach for Authenticating Textual or Graphical Passwords Using Hopfield Neural Network

TL;DR

This paper introduces a Hopfield neural network-based method for authenticating textual and graphical passwords, demonstrating improved accuracy and response time over existing neural network techniques.

Contribution

It proposes a novel Hopfield network scheme for password authentication using probabilistic password representations, enhancing performance over layered neural networks.

Findings

Better accuracy in password authentication

Faster response time during registration and password updates

Effective use of probabilistic values for password inputs

Abstract

Password authentication using Hopfield Networks is presented in this paper. In this paper we discussed the Hopfield Network Scheme for Textual and graphical passwords, for which input Password will be converted in to probabilistic values. We observed how to get password authentication using Probabilistic values for Textual passwords and Graphical passwords. This study proposes the use of a Hopfield neural network technique for password authentication. In comparison to existing layered neural network techniques, the proposed method provides better accuracy and quicker response time to registration and password changes.