A Comparative Analysis of Soft computing techniques for Password Strength Classification

Passwords are a very important sensitive aspect in computer security. Password is the important identitiy in authenticating any individual in different domains. It plays a vital role in accessing any device or any software. Users around the world are consistently told about the necessity of a strong password to protect their access with unauthorized users. In this paper, we propose a comparative analysis of soft computing techniques such as Back propagation neural network (BPN), Logistic regression (LR), Hopfield neural network (HNN), Brain state in a box (BSB) and associative memory network (BAM), Convolution neural network (CNN) system for strong password classification. The dataset contains only a single attribute of passwords with 3 classes as weak, medium and strong. The experimental results analysis shows that for such a datset, the simple logistic regression model yields better output when compared with Convolution Neural networks, Back propagation neural network, Logistic regression, Hopfield neural network, Brain state in a box and Bidirectional Associative Memory. It can be concluded that logistic regression is adapted for such an application and also plays as a better prediction system for such a dataset.