A FRAMEWORK FOR ADVANCED PASSWORD STRENGTH VALUATION: INTEGRATING RULE-BASED CRITERIA AND AI-DRIVEN NAMED ENTITY RECOGNITION TO MITIGATE SECURITY VULNERABILITIES
Abstract
This study introduces an advanced framework that not only evaluates password strength but
also tackles a critical issue: personal information embedded in passwords. By incorporating
NER, the framework surpasses existing tools by detecting personal data within passwords,
offering a more detailed assessment. These insights contribute to better password creation and
evaluation strategies, reinforcing the importance of sophisticated mechanisms in fortifying
digital security practices.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.