An Intelligent Machine Learning Framework for Password Strength and Security System

Password-based authentication remains the primary security mechanism for protecting user accounts and sensitive information, yet weak password selection continues as a pervasive global vulnerability. Traditional rule-based password strength meters exhibit significant limitations in adapting to emerging attack strategies and identifying subtle vulnerability patterns. This study presents an intelligent Long Short-Term Memory (LSTM) based framework for password strength classification that addresses these shortcomings through advanced Deep Learning techniques. The proposed system integrates a bidirectional LSTM network with comprehensive feature extraction pipelines incorporating Shannon entropy calculations, pattern recognition metrics, and statistical analysis to assess password security across weak, medium, and strong classifications. The framework was trained and validated on a diverse dataset of 1,000,000 passwords compiled from breach databases and synthetic sources, partitioned into 70% training, 15% validation, and 15% testing sets. The experimental results for the system demonstrate exceptional performance with 96.8% accuracy, outperforming traditional rule-based systems by 2