Adversarial Machine Learning for Robust Password Strength Estimation

TL;DR

This paper develops robust password strength estimation models using adversarial machine learning, demonstrating improved accuracy and highlighting the importance of adversarial training for security against adaptive threats.

Contribution

It introduces the application of adversarial machine learning to password strength estimation, enhancing model robustness against deceptive password attacks.

Findings

Adversarial training improves classification accuracy by up to 20%.

Models trained on adversarial passwords are more robust against attacks.

The study emphasizes integrating adversarial techniques into security systems.

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. Keywords: adversarial attack, password strength, classification, machine learning