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All Journal International Journal Of Computer, Network Security and Information System (IJCONSIST)
Tanjung Arswendo Yudha
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Computer Science & IT

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Machine Learning for Password Strength Classification Using Length and Entropy Tanjung Arswendo Yudha; Reyhan, Muhamad; Mutmainnah, Dianisa; Hakiem, Nashrul
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.139

Abstract

Password security is a critical cybersecurity challenge due to the prevalence of user-generated weak credentials, so automated evaluation methods are needed. This paper develops a Random Forest classification model to predict password strength based on two main features, namely password length and Shannon entropy, trained on a large-scale public dataset. The model achieved a classification accuracy of 91.5% on the test data, where feature importance analysis identified entropy as the most significant predictor. The resulting high-accuracy model is suitable for integration into real-time password strength feedback systems and provides a quantitative basis for formulating stronger security policies.
Co-Authors Mutmainnah, Dianisa Nashrul Hakiem Reyhan, Muhamad