{"ID":2842932,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09492","arxiv_id":"2511.09492","title":"Enhancing Password Security Through a High-Accuracy Scoring Framework Using Random Forests","abstract":"Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns (e.g., 'P@ssw0rd1!'), giving users a false sense of security. To address this, we implement and evaluate a password strength scoring system by comparing four machine learning models: Random Forest (RF), Support Vector Machine (SVM), a Convolutional Neural Network (CNN), and Logistic Regression with a dataset of over 660,000 real-world passwords. Our primary contribution is a novel hybrid feature engineering approach that captures nuanced vulnerabilities missed by standard metrics. We introduce features like leetspeak-normalized Shannon entropy to assess true randomness, pattern detection for keyboard walks and sequences, and character-level TF-IDF n-grams to identify frequently reused substrings from breached password datasets. our RF model achieved superior performance, achieving 99.12% accuracy on a held-out test set. Crucially, the interpretability of the Random Forest model allows for feature importance analysis, providing a clear pathway to developing security tools that offer specific, actionable feedback to users. This study bridges the gap between predictive accuracy and practical usability, resulting in a high-performance scoring system that not only reduces password-based vulnerabilities but also empowers users to make more informed security decisions.","short_abstract":"Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns (e.g., 'P@ssw0rd1!'), giving users a false sense of security. To address this, we imple...","url_abs":"https://arxiv.org/abs/2511.09492","url_pdf":"https://arxiv.org/pdf/2511.09492v2","authors":"[\"Muhammed El Mustaqeem Mazelan\",\"Noor Hazlina Abdul\",\"Nouar AlDahoul\"]","published":"2025-11-12T17:05:27Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.AI\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}