DDyn - Dynamic DNS Updater

DDyn is a dynamic DNS updater application designed to automatically update DNS records when the public IP address changes. The application leverages machine learning to predict IP changes and monitor connectivity, ensuring reliable updates to DNS services.

Table of Contents

• Features
• Installation
• Usage
• Configuration
• Machine Learning Integration
• License

Features

• Dynamic DNS Updates: Automatically updates DNS records when the public IP address changes using DNS-O-Matic as the provider. This allows users to manage multiple DNS records across various services from a single interface.
• Machine Learning Predictions: Utilizes machine learning models to predict IP changes based on historical data.
• Anomaly Detection: Identifies unusual patterns in IP changes to enhance reliability.
• Command-Line Interface: Provides a simple CLI for managing updates and models.

Installation

1. Clone the repository:

   git clone https://github.com/iap/ddyn.git
   cd ddyn

2. Create and activate a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # Linux/Mac
   # or
   .venv\Scripts\activate     # Windows

3. Install the required dependencies:

   pip install -r requirements.txt

4. Configure the environment variables:

   cp .env.example .env
   # Edit .env with your DNS-O-Matic credentials and other settings

Usage

1. Start the DNS updater:

   python -m src.main

2. Manage machine learning models using the CLI:

   python -m src.cli train          # Train the ML models
   python -m src.cli status         # Check model status
   python -m src.cli predict        # Get predictions

Configuration

• Environment Variables: Configure your .env file with the following variables:

  DNSOMATIC_USERNAME=your_username
  DNSOMATIC_PASSWORD=your_password
  DNSOMATIC_HOSTNAME=your_hostname
  

Machine Learning Integration

• The application uses machine learning models to predict IP changes and enhance connection reliability.
• Current models include:
  • Random Forest Classifier: For predicting IP changes based on historical data.
  • Isolation Forest: For detecting anomalies in IP change patterns.

License

This project is licensed under the GNU General Public License (GPL) Version 3. See the LICENSE file for details.