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PWLDS

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Password
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Strength_Level
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PWLDS (Password Weakness and Level Dataset)

PWLDS is a public dataset consisting of over 4 million passwords with varying assigned strength levels. The dataset was designed to help researchers, security professionals, and developers analyze password strength and build more secure systems. The dataset contains 5 classes:

  • very_weak: Represented by 0 in the dataset
  • weak: Represented by 1 in the dataset
  • average: Represented by 2 in the dataset
  • strong: Represented by 3 in the dataset
  • very_strong: Represented by 4 in the dataset

For strength level 4, we used Python's secrets module to generate cryptographically secure passwords, ensuring the robustness and security of these passwords.

Intended Uses

  • Security Analysis: PWLDS can be used to study common password patterns and weaknesses, helping to identify vulnerabilities and improve password policies.
  • Machine Learning: The dataset can be used to train machine learning models for password strength estimation or prediction.
  • Educational Purposes: This dataset is valuable for educational projects and demonstrations related to cybersecurity and data science.
  • Benchmarking: PWLDS provides a large, labeled dataset for benchmarking password strength estimation algorithms and tools.

Limitations

  • No Real-World Data: The dataset is synthetically generated and does not contain real user passwords. While this avoids privacy concerns, it may not fully represent real-world password distribution and usage patterns.
  • Bias in Password Generation: Since passwords are generated based on predefined rules and patterns, there may be biases that do not reflect the diversity of passwords used in real-life scenarios.
  • Exclusivity to English Words: The weak and very weak password categories rely heavily on English words, which may not be representative of non-English-speaking populations.

Citation

When using this dataset, please cite as follows:

Dataset Title: Password Weakness and Level Dataset (PWLDS)
Author: Infinitode Pty Ltd
Date: 2024
Source: https://github.com/Infinitode/PWLDS
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
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Number of rows:
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