Classifying user-created passwords using machine learning and natural language processing techniques

Abstract

Passwords are the dominant authentication method. However, evaluating the strength of user-created passwords remains a significant challenge due to the influence of various external factors, such as language, culture, and keyboard layout. In this paper, we address the problem of classifying user-created passwords into predefined groups, rather than directly evaluating their strength. First, we assess the performance of classifiers utilizing eight machine learning (ML) algorithms and four Natural Language Processing techniques to identify the optimal combination of ML algorithms and feature extraction methods. Through this experiment, we determine that the classifier combining Bag-of-Words and Logistic Regression is the most effective approach for classifying user-created passwords. Subsequently, we propose a hierarchical classification model to enhance the performance of this classifier. Experimental results demonstrate that the proposed model achieves accuracy of 97.81 % and recall of 99.66 % for weak passwords.