Adversarial Machine Learning For Robust Password Strength Estimation

Authors

• Pappu Jha, School of Computing Sciences and Computer Engineering
• Hanzla Hamid, School of Computing Sciences and Computer Engineering
• Oluseyi Olukola, School of Computing Sciences and Computer Engineering
• Ashim Dahal, School of Computing Sciences and Computer Engineering
• Nick Rahimi, School of Computing Sciences and Computer Engineering

Document Type

Conference Proceeding

Publication Date

1-1-2026

School

Computing Sciences and Computer Engineering

Abstract

Passwords remain one of the most common methods for securing sensitive data in the digital age. However, weak password choices continue to pose significant risks to data security and privacy. This study aims to solve the problem by focusing on developing robust password strength estimation models using adversarial machine learning, a technique that trains models on intentionally crafted deceptive passwords to expose and address vulnerabilities posed by such passwords. We apply five classification algorithms and use a dataset with more than 670,000 samples of adversarial passwords to train the models. Results demonstrate that adversarial training improves password strength classification accuracy by up to 20% compared to traditional machine learning models. It highlights the importance of integrating adversarial machine learning into security systems to enhance their robustness against modern adaptive threats.

Publication Title

Communications in Computer and Information Science

Volume

2720 CCIS

First Page

289

Last Page

301

Recommended Citation

Jha, P., Hamid, H., Olukola, O., Dahal, A., Rahimi, N. (2026). Adversarial Machine Learning For Robust Password Strength Estimation. Communications in Computer and Information Science, 2720 CCIS, 289-301.
Available at: https://aquila.usm.edu/fac_pubs/22100