Password Strength Evaluation via Zipf's Law and Password Entropy

Abstract

Password strength assessment is pivotal for safeguarding information security. While conventional methods primarily emphasize password length and character diversity, they often overlook character distribution patterns. Our paper introduces a novel password strength evaluation method leveraging Zipf's Law and the password entropy model. Despite the importance of password strength evaluation in ensuring information security, current methods frequently rely solely on password length and character combinations, disregarding character distribution patterns. By integrating Zipf's Law, commonly observed in natural language and passwords, into our evaluation framework, we propose a more comprehensive and precise method. Through experiments utilizing diverse password datasets and comparative analysis with traditional approaches, we validate the superior accuracy and reliability of our proposed method in assessing password strength. This research holds significance in enhancing password security assessment methodologies and offers more effective support for password management and information security.