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Abstract
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 23.4 percentage points and commercial password meters by 12.1-18.5 percentage points. The system achieves 98.3% recall for weak password detection with only 1.7% false negative rate, compared to 8.8-13.9% for commercial solutions. Field deployment with 500 users over 30 days validated practical impact, showing 40.7% higher password entropy, 204.3% improvement in strong password adoption, and 69.5% reduction in dictionary word usage. The lightweight architecture (4.7 MB model size, 1.8ms inference time) enables real-time integration into web applications and enterprise authentication systems. The framework provides actionable, context-aware security recommendations and achieves detection rates exceeding 94% across diverse attack vectors including dictionary attacks, keyboard patterns, sequential characters, and previously breached credentials. The study demonstrates that LSTM-based approaches significantly enhance password security assessment capabilities and effectively guide users toward adopting stronger authentication practices.
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References
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