Password Strength Analysis and Prediction Using Machine Learning
Password Strength, Machine Learning, Cybersecurity, Feature Engineering, Random Forest, Gradient Boosting, Authentication, Password Classification
Password security remains one of the most critical challenges in digital authentication systems. Despite widespread awareness campaigns, users continue to choose weak and predictable passwords, exposing sensitive data to brute-force and dictionary-based attacks. This paper presents a machine learning–based framework for analyzing and predicting password strength using a structured, real-world dataset. We evaluate multiple supervised learning classifiers — including Random Forest, Gradient Boosting, Support Vector Machine (SVM), and Logistic Regression — trained on engineered features derived from password characteristics such as length, entropy, character diversity, and pattern frequency. Our experiments demonstrate that all evaluated classifiers achieve 100% classification accuracy on this dataset, a result attributable to the deterministic nature of the dataset's labeling scheme, which is directly learnable from structural password features such as length and character class composition. The study provides insights into which linguistic and structural features most strongly correlate with password vulnerability and discusses implications for improving authentication policies and user-facing strength meters.
Registration ID: IJVRA_702271 Published ID: IJVRA2603728
"Password Strength Analysis and Prediction Using Machine Learning", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.596-599, March-2026, Available :https://ijpub.org/ijvra/papers/IJVRA2603728.pdf
Paper Reg. ID: IJVRA_702271
Published Paper Id: IJVRA2603728
Research Area: Other area not in list
Country: Pune, Maharashtra, India
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
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Publisher: IJVRA (IJ Publication) Janvi Wave
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