Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{208752,
        author = {Vaishnavi Gade and Vaibhavi Khandagle and Sanket Rupnar and Prof. Jyoti Sarwade},
        title = {Predicting Password Strength Using Machine Learning: A Data-Driven Approach to Cybersecurity Hygiene},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {644-648},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=208752},
        abstract = {Weak passwords remain one of the leading causes of account compromise and unauthorized access in digital systems. Traditional password strength meters rely on static, rule-based heuristics (e.g., length, character variety) that often fail to capture the true unpredictability or “crackability” of a password. This paper proposes a machine learning-based approach to predict password strength by analyzing structural and statistical features such as length, character diversity, entropy, and common pattern usage. Using a labeled dataset of passwords categorized into weak, medium, and strong classes, classification models including Logistic Regression, Random Forest, and XGBoost are trained and evaluated based on accuracy, precision, recall, and F1-score. Experimental results demonstrate that ensemble-based models more effectively capture the non-linear relationships between password features and strength categories compared to rule-based systems. The findings highlight the potential of data-driven password strength estimation to improve user authentication security and guide the design of smarter, adaptive password policies.},
        keywords = {Password Security, Machine Learning, Cybersecurity, Data Analytics, Password Strength Classification, Authentication},
        month = {September},
        }