Machine Learning for Password Strength Classification Using Length and Entropy

  • Tanjung Arswendo Yudha UIN Syarif Hidayatullah Jakarta
  • Muhamad Reyhan Department of Informatics Engineering Syarif Hidayatullah State Islamic University Jakarta, Indonesia South Tangerang, Banten
  • Dianisa Mutmainnah Department of Informatics Engineering Syarif Hidayatullah State Islamic University Jakarta, Indonesia South Tangerang, Banten
  • Nashrul Hakiem Department of Informatics Engineering Syarif Hidayatullah State Islamic University Jakarta, Indonesia South Tangerang, Banten
DOI: https://doi.org/10.33005/ijconsist.v5i1.139
Keywords: Cybersecurity, Machine Learning, Password Security, Random Forest, Feature Engineering, Shannon Entropy

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.

Published
2023-09-01
How to Cite
Tanjung Arswendo Yudha, Reyhan, M., Mutmainnah, D., & Hakiem, N. (2023). Machine Learning for Password Strength Classification Using Length and Entropy. IJCONSIST JOURNALS, 5(1), 39-45. https://doi.org/10.33005/ijconsist.v5i1.139
Issue
Vol 5 No 1 (2023): September
Section
Articles

Copyright (c) 2023 Tanjung Arswendo Yudha, Muhamad Reyhan, Dianisa Mutmainnah, , Nashrul Hakiem

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.