Password Strength Classification Using Machine Learning and Cryptography

Classifying password strengths is essential for preventing credential-based assaults. This study suggests a machine learning approach that uses n-gram analysis and cryptographic entropy to classify passwords into weak, medium, and strong categories in order to overcome this difficulty. After being trained on 670,000 Kaggle passwords, the Random Forest model identified strong passwords with 99.9% accuracy. The use of TF-IDF vectorization to identify sequential patterns and the incorporation of Shannon entropy to measure password unpredictability are important advancements. The model makes use of these features to discover intricate connections between password strength and properties. SHA-256 hashing ensures ethical considerations, including GDPR compliance. This cryptographic-ML method fills in holes in cross-cultural password analysis and improves password evaluation for real-world authentication systems.