Password Strength Classification Using Machine Learning Methods
Habiba Rehman, Adnan Ahmed, Athar Hussain, Muhamamd Umar, Mohammad Siraj, Waqas Ahmed, Mir Muhamnad, Eesha Rehman · 2024
Passwords remain a critical component of authentication systems due to their ease of implementation, despite the availability of more secure methods such as biometrics and smart cards. However, password-based systems are vulnerable to various attacks due to the predictable patterns users often employ. This vulnerability necessitates the development of robust strategies to enforce strong passwords. This research focuses on modeling password strength prediction as a classification task using multiple supervised machine learning algorithms. The goal is to classify passwords into categories of weak, medium, and strong, thereby enhancing the security of systems against online and offline attacks. The result findings demonstrate that Artificial Neural Networks (ANN) and Random Forest (RF) significantly outperform their counterparts in predicting password strength, achieving accuracy rates up to 98.97%. This study aims to provide insights into effective password management and underscore the potential of machine learning in enhancing cybersecurity.