Installation

This guide covers the installation and setup of the RL-IDS (Reinforcement Learning Intrusion Detection System).

System Requirements

Minimum Requirements

• Operating System: Linux (Ubuntu 18.04+), macOS (10.15+), or Windows 10/11
• Python: 3.13.0+ (recommended: 3.13.0)
• Memory: 8GB RAM minimum, 16GB recommended
• Storage: 10GB free space for datasets and models
• Network: Administrative privileges for packet capture

Hardware Recommendations

• CPU: Multi-core processor (8+ cores recommended for training)
• GPU: NVIDIA GPU with CUDA support (optional, for faster training)
• Network Interface: Ethernet adapter for network monitoring

Installation Methods

Method 1: pip Installation (Recommended)

# Create virtual environment
python -m venv rl_ids_env
source rl_ids_env/bin/activate  # Linux/macOS
# or
rl_ids_env\Scripts\activate     # Windows

# Install RL-IDS
pip install rl_ids

# Verify installation
python -c "import rl_ids; print('RL-IDS installed successfully')"

Method 2: Development Installation

# Clone the repository
git clone https://github.com/yashpotdar-py/rl-ids.git
cd rl-ids

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/macOS
# or
venv\Scripts\activate     # Windows

# Install in development mode
pip install -e .

# Install additional dependencies
pip install -r requirements.txt

Method 3: Docker Installation (Coming Soon)

# Pull the Docker image
docker pull yashpotdar/rl-ids:latest

# Run the container
docker run -p 8000:8000 yashpotdar/rl-ids:latest

Post-Installation Setup

1. Verify Network Permissions

For network monitoring capabilities, ensure you have the necessary permissions:

# Linux: Check if user can capture packets
sudo setcap cap_net_raw,cap_net_admin=eip $(which python)

# Or run with sudo (not recommended for production)
sudo python network_monitor.py

2. Download Pre-trained Models

# Download the latest DQN model (if available)
python -c "
from rl_ids.modeling.train import download_pretrained_model
download_pretrained_model()
"

3. Configure Environment

Create a .env file in your project directory:

# API Configuration
API_HOST=localhost
API_PORT=8000
DEBUG=true

# Model Configuration
MODEL_PATH=models/dqn_model_best.pt
FEATURE_SCALER_PATH=models/feature_scaler.pkl

# Monitoring Configuration
CAPTURE_INTERFACE=eth0
LOG_LEVEL=INFO
LOG_FILE=logs/rl_ids.log

4. Test Installation

Run the test suite to verify everything is working:

# Basic functionality test
pytest tests/test_api.py -v

# Complete test suite (if available)
python -m pytest tests/ -v

Configuration

Network Interface Configuration

Identify available network interfaces:

# List network interfaces
python -c "
import psutil
interfaces = psutil.net_if_addrs()
for interface, addresses in interfaces.items():
    print(f'Interface: {interface}')
    for addr in addresses:
        if addr.family.name == 'AF_INET':
            print(f'  IP: {addr.address}')
"

GPU Configuration (Optional)

If you have an NVIDIA GPU and want to use CUDA acceleration:

# Install PyTorch with CUDA support
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

# Verify CUDA availability
python -c "
import torch
print(f'CUDA available: {torch.cuda.is_available()}')
if torch.cuda.is_available():
    print(f'CUDA version: {torch.version.cuda}')
    print(f'GPU device: {torch.cuda.get_device_name(0)}')
"

Troubleshooting

Common Issues

Permission Denied Error

# Error: [Errno 1] Operation not permitted
# Solution: Run with appropriate permissions or set capabilities
sudo setcap cap_net_raw,cap_net_admin=eip $(which python)

Import Error

# Error: ModuleNotFoundError: No module named 'rl_ids'
# Solution: Ensure virtual environment is activated and package is installed
source venv/bin/activate
pip install -e .

CUDA Out of Memory

# Error: RuntimeError: CUDA out of memory
# Solution: Reduce batch size or use CPU
export CUDA_VISIBLE_DEVICES=""  # Force CPU usage

Network Interface Not Found

# Error: Interface 'eth0' not found
# Solution: List available interfaces and update configuration
python -c "import psutil; print(list(psutil.net_if_addrs().keys()))"

Logging Configuration

Enable detailed logging for troubleshooting:

# Add to your Python script
import logging
from loguru import logger

# Configure logging level
logger.add("logs/debug.log", level="DEBUG", rotation="10 MB")

Performance Optimization

For Training

• Use GPU acceleration when available
• Increase batch size if memory permits
• Use multiple CPU cores for data preprocessing

For Monitoring

• Adjust capture buffer size based on network traffic
• Use appropriate logging levels to reduce overhead
• Consider using background processes for real-time monitoring

Next Steps

After successful installation:

1. Read the Network Monitoring Guide to start monitoring network traffic
2. Explore the API Documentation to integrate with existing systems
3. Check the Agent Configuration to customize the RL model
4. Review Development Setup if you plan to contribute

Getting Help

• Documentation: Browse the complete documentation in this site
• Issues: Report bugs on the GitHub Issues page
• Discussions: Join community discussions on the repository

Version Information

• Current Version: 1.2.0
• Python Compatibility: 3.10.0+
• Last Updated: 2025