🐝 Honeypot Project

This project is a web-based honeypot designed to detect and log suspicious activities while analyzing patterns using machine learning. The project features real-time prediction, data visualization, and detailed logging to enhance security monitoring.

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🚀 Features

1. Honeypot Web Interface: A decoy website to attract malicious activities.
2. Real-Time Monitoring: Logs activity with IP addresses, usernames, and user agents.
3. Machine Learning Integration: Predicts whether an action is malicious using a trained Random Forest model.
4. Data Visualization: Analyzes and visualizes suspicious activity trends.
5. Log Management: Stores detailed logs in CSV files for further analysis.

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🗃️ Project Structure

.
├── app/                # Application scripts (log handling, monitoring)
├── images/             # Image assets used on the website
├── logs/               # Logs and CSV files for activity tracking
├── ml/                 # Machine learning scripts and models
├── php/                # PHP scripts for login, registration, and page handling
├── visualize/          # Scripts for visualizing data
├── db.php              # Database connection file
├── honeypot.py         # Honeypot main script
├── index.html          # Main web page
├── prediction.js       # Prediction handling script
├── style.css           # CSS for styling the honeypot pages
├── submit.php          # PHP script for handling form submissions
└── README.md           # Project documentation

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🛠️ Setup Instructions

Prerequisites

• Python 3.x
• XAMPP (for Apache and MySQL)
• Streamlit (for visualization)
  Run the following command to install all necessary dependencies:

  pip install -r requirements.txt
  

Steps to Run

1. Clone the repository:

   git clone https://github.com/yourusername/HoneyPot.git
   cd HoneyPot
   

2. Start XAMPP:

   • Run Apache and MySQL from the XAMPP Control Panel.

3. Database Setup:

   • Import the database using db.php or manually through phpMyAdmin.

4. Run the Honeypot Server:

   python honeypot.py
   

   • Visit http://localhost/HoneyPot in your browser.

5. Start the Machine Learning API:

   python ml/ml_api.py
   

   • The API will run on http://127.0.0.1:5000/predict.

6. Visualize Logs:

   streamlit run visualize/visualize_logs.py
   

   • Access the visualization dashboard at http://localhost:8501.

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📊 Usage

• Access the honeypot web interface to attract and log suspicious activity.
• Real-time predictions are logged automatically in the CSV file.
• Visualize patterns and suspicious activity trends via Streamlit.

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💡 Troubleshooting

• API Connection Issues: Ensure that the API server is running and accessible at http://127.0.0.1:5000.
• Log Not Generated: Check file paths and directory permissions.
• Visualization Errors: Verify that Streamlit is running properly.

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📝 Acknowledgments

Special thanks to the open-source libraries and tools that made this project possible, including:

• Flask for API creation
• Scikit-learn for machine learning
• Pandas for data processing
• Streamlit for visualization

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