Password Strength Detection via Machine Learning: Analysis, Modeling, and Evaluation Article Swipe
Jianhua Mo
,
Xiaoqi Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.16439
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.16439
As network security issues continue gaining prominence, password security has become crucial in safeguarding personal information and network systems. This study first introduces various methods for system password cracking, outlines password defense strategies, and discusses the application of machine learning in the realm of password security. Subsequently, we conduct a detailed public password database analysis, uncovering standard features and patterns among passwords. We extract multiple characteristics of passwords, including length, the number of digits, the number of uppercase and lowercase letters, and the number of special characters. We then experiment with six different machine learning algorithms: support vector machines, logistic regression, neural networks, decision trees, random forests, and stacked models, evaluating each model's performance based on various metrics, including accuracy, recall, and F1 score through model validation and hyperparameter tuning. The evaluation results on the test set indicate that decision trees and stacked models excel in accuracy, recall, and F1 score, making them a practical option for the strong and weak password classification task.
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Metadata
- Type
- preprint
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- en
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- http://arxiv.org/abs/2505.16439
- https://arxiv.org/pdf/2505.16439
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4416451790
All OpenAlex metadata
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Password Strength Detection via Machine Learning: Analysis, Modeling, and EvaluationWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-05-22Full publication date if available
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Jianhua Mo, Xiaoqi LiList of authors in order
- Landing page
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https://arxiv.org/abs/2505.16439Publisher landing page
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https://arxiv.org/pdf/2505.16439Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2505.16439Direct OA link when available
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0Total citation count in OpenAlex
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