password-list-generator-complete

pass list generator tool for Ethical Hacking
under development

🔐 PLG_ysnrfd – Advanced Context-Aware Password Intelligence & Security Analyzer

🚀 Overview

PLG_ysnrfd is a powerful tool for password security analysis and context-aware password generation.
It leverages advanced algorithms for entropy calculation, pattern recognition, user behavior modeling, and cultural context to produce strong, personalized passwords and evaluate the strength of existing ones.

This project is designed to improve cybersecurity awareness and is intended only for authorized security testing and educational purposes.

✨ Features

Comprehensive Password Analysis
Detects dictionary words, keyboard patterns, repeated characters, cultural references, and common password structures.
Entropy Calculation
Calculates the password entropy based on Shannon’s formula and penalizes weak or predictable patterns.
Smart Password Generation
Creates personalized passwords using user information (e.g., names, pets, dates, cultural events) with advanced transformations (leet-speak, camelCase, hex encoding, etc.).
Multi-Language Support
Supports English, German, Persian, French, and Spanish with language-specific wordlists and keyboard layouts.
Ethical Safeguard System
Built-in interactive verification to ensure usage is legal and ethical (authorized penetration testing).
Behavioral Prediction
Uses a UserBehaviorPredictor class to understand the security awareness, emotional bias, and cultural context of the user.
Encrypted Logging
Secure logging of usage data with SHA-256 hashing.
Leaked Password Transformation
Learns from compromised passwords and generates improved, stronger variants.

📦 Installation

1. Clone the repository

git clone https://github.com/ysnrfd/password-list-generator-complete.git
cd password-list-generator-complete

2. Install dependencies

pip install -r requirements.txt

3. Required Python Libraries

nltk (WordNet, Punkt tokenizer)

pandas

scikit-learn

requests

tqdm

python-Levenshtein

Note: For NLTK, you may need to download WordNet and Punkt data:

import nltk
nltk.download('wordnet')
nltk.download('punkt')

🔧 Usage

1. Run the tool directly

Windows:

python PLG_ysnrfd.py

Linux:

python3 PLG_ysnrfd.py

2. Analyze the strength of a password

from PLG_ysnrfd import PasswordEntropyAnalyzer

analyzer = PasswordEntropyAnalyzer(language='en')
result = analyzer.analyze_password_patterns("MyP@ssw0rd2024")
print(result)

3. Generate context-aware passwords

from PLG_ysnrfd import ContextualPasswordGenerator

user_info = {
    'first_name': 'Alice',
    'birth_year': '1995',
    'pets': ['Luna'],
    'nationality': 'USA',
    'language': 'en'
}

generator = ContextualPasswordGenerator(language='en')
passwords = generator._generate_weighted_combinations(user_info, count=10, min_length=8, max_length=16)
print(passwords)

⚠️ Ethical Disclaimer

This tool is strictly for educational and authorized penetration testing. Before usage, the program requires you to accept the Ethical Usage Agreement. Unauthorized use of this tool is illegal and unethical. Always ensure you have explicit written consent before testing any system.

📂 Project Structure

password-list-generator-complete/
│── PLG_ysnrfd.py
│── requirements.txt
│── README.md

🧠 Algorithms

Entropy Calculation
Uses Shannon entropy, keyboard pattern detection, and character frequency analysis to assess password complexity.
User Behavior Modeling
Predicts password habits based on cultural background, age, pets, children, and emotional factors.
Context-Aware Password Generation
Combines personal data, cultural events, and randomized transformations to produce strong, unique passwords.

📊 Roadmap

Integration with leaked password databases (HaveIBeenPwned API)
More language packs (Italian, Russian, Arabic)
Plugin-based architecture for custom rules

📝 License

This project is released under the ysnrfd LICENSE. See the LICENSE file for details.

👤 Author

Developer: YSNRFD
Telegram: @ysnrfd

🌟 Support the Project

If you find this project useful, please give it a ⭐ on GitHub!