Changelog

All notable changes to the RL-IDS (Reinforcement Learning Intrusion Detection System) are documented here.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[1.2.0] - 2025-06-27

Added

• Enterprise-Grade CI/CD Infrastructure
• Multi-job CI pipeline with linting, testing, package validation, and security scanning
• Automated documentation deployment to GitHub Pages with proper caching
• Professional release workflow with version validation and artifact management
• Scheduled dependency update automation with PR creation

• Comprehensive quality gates with Ruff, pytest, safety, and bandit integration

• Professional Development Workflow

• Automated code quality checks on every pull request
• Coverage reporting with Codecov integration
• Package build validation and installation testing
• Security vulnerability scanning with industry-standard tools

• Efficient workflow execution with concurrency control and caching

• Automated Release Management

• GitHub releases with automatic changelog extraction
• Version consistency validation across project files
• Build artifact creation and distribution
• Support for both automatic tag-based and manual releases

• Proper semantic versioning and pre-release handling

• Marketing-Quality README.md

• Stunning visual design with professional branding and hero section
• Interactive feature comparison tables with visual elements
• Mermaid architecture diagrams for system visualization
• Performance metrics dashboard with real statistics (95.3% accuracy, <100ms response time)
• Comprehensive quick start guide with 30-second demo
• Attack type detection matrix with detailed capabilities
• Professional project structure visualization with directory tree

• Enterprise-ready presentation with Docker deployment examples

• Advanced API Integration Examples

• Real-world SIEM integration patterns with security tool connectivity
• High-performance batch processing examples with connection pooling
• Enterprise monitoring and alerting implementation guides
• Production-ready error handling and retry patterns
• Scalability examples for distributed deployments

• Security best practices for API integration

• Enhanced User Experience

• Beautiful visual hierarchy with consistent emoji usage
• Collapsible sections for detailed technical information
• Comprehensive benchmarks and performance characteristics
• Development roadmap with planned feature enhancements
• Community and support section with clear contact channels

• Professional call-to-action elements for GitHub engagement

• Complete Documentation Coverage

• Installation guide with multiple deployment methods
• Architecture documentation with system design principles
• Testing guide with comprehensive examples and best practices
• FAQ section covering all common use cases and troubleshooting
• Module documentation for all core components
• API examples with real-world integration scenarios

Changed

• Visual Design Revolution
• Complete README overhaul with marketing-quality presentation
• Enhanced documentation structure for better user journey
• Improved code examples with proper syntax highlighting
• Better navigation with linked sections and table of contents

• Consistent branding and professional appearance throughout

• Content Quality Enhancement

• Updated all documentation for accuracy and completeness
• Improved technical examples with real-world applicability
• Enhanced cross-references and navigation between sections
• Better organization of complex technical information

Fixed

• Documentation consistency across all files and sections
• Code example accuracy and comprehensive testing
• Installation instructions for all supported platforms
• Link validation and cross-reference accuracy throughout documentation

[1.1.0] - 2025-06-21

Added

• Comprehensive Documentation Suite
• Complete MkDocs-based documentation with shadcn theme
• Interactive API reference with OpenAPI integration
• Step-by-step installation and setup guides
• Advanced user guides for network and website monitoring
• Production deployment guides and best practices
• FAQ and troubleshooting section with detailed solutions

• Module-level documentation for agents, features, and environments

• Development & Contribution Infrastructure

• GitHub issue templates for bugs and feature requests
• Comprehensive contributing guidelines with development setup
• Pre-commit hooks configuration for code quality
• Automated testing and CI/CD pipeline documentation

• GitHub Actions workflows for documentation deployment

• Enhanced API Documentation

• Detailed endpoint documentation with examples
• Python client library usage guides
• Request/response model specifications
• Error handling and status code documentation

• Security and authentication guidelines

• Architecture Documentation

• System architecture overview with diagrams
• Component interaction documentation
• Data flow and processing pipelines
• Configuration management guides
• Performance characteristics and optimization

Changed

• Improved logging configuration with loguru integration
• Enhanced model checkpoint saving strategies
• Restructured documentation for better user experience
• Updated API documentation with comprehensive examples
• Reorganized project structure for better maintainability

Fixed

• Training progress monitoring accuracy
• API health check endpoint reliability
• Documentation links and cross-references
• Code examples and snippets consistency
• Installation instructions for different platforms

[1.0.0] - 2025-06-21

Added

• Core RL-IDS System
• DQN agent with Deep Q-Network implementation
• Custom Gymnasium environment for CICIDS2017 dataset
• Feature extraction pipeline with 78 network flow features
• Real-time network packet capture and analysis

• Comprehensive training and evaluation framework

• Network Monitoring

• Live packet capture using raw sockets
• Protocol analysis (TCP, UDP, HTTP, HTTPS)
• Flow-based traffic aggregation
• Statistical feature computation

• Real-time threat detection and alerting

• Website Monitoring

• Automated web request generation
• Traffic pattern simulation
• Packet capture for generated traffic

• Integration with network monitoring pipeline

• FastAPI Web Service

• RESTful API for predictions and model information
• Health check endpoints for monitoring
• Batch prediction capabilities
• Comprehensive error handling and validation

• OpenAPI/Swagger documentation

• Python Client Library

• Synchronous and asynchronous client implementations
• Built-in retry mechanisms and error handling
• Comprehensive type hints and documentation

• Integration examples and best practices

• Machine Learning Pipeline

• CICIDS2017 dataset processing and feature extraction
• DQN training with experience replay
• Model evaluation and performance metrics
• Hyperparameter optimization support

• Model checkpointing and versioning

• Data Processing

• Flow-based feature extraction from network packets
• Statistical analysis of traffic patterns
• Data preprocessing and normalization
• Train/validation/test dataset splitting

• Feature importance analysis

• Configuration Management

• Environment-based configuration
• Flexible model and training parameters
• Network interface and monitoring settings
• API server configuration
• Logging and debugging options

Technical Features

• Reinforcement Learning
• Deep Q-Network (DQN) implementation
• Experience replay buffer for stable training
• Target network for improved convergence
• Epsilon-greedy exploration strategy

• Reward-based learning for threat detection

• Network Analysis

• 78 CICIDS2017-compatible features
• Flow duration and packet timing analysis
• Protocol-specific feature extraction
• Bidirectional flow analysis

• Statistical traffic characterization

• Real-time Processing

• Live packet capture and processing
• Streaming feature extraction
• Real-time prediction pipeline
• Configurable monitoring intervals

• Efficient memory management

• Performance Optimization

• Vectorized operations for feature extraction
• Efficient data structures for packet processing
• Optimized model inference
• Configurable batch processing
• Memory-efficient data handling

Supported Attack Types

• DDoS Attacks: Distributed Denial of Service detection
• Port Scanning: Network reconnaissance identification
• Web Attacks: SQL injection, XSS, and web-based threats
• Infiltration: Advanced persistent threat detection
• Brute Force: Authentication and password attacks
• Botnet: Command and control communication detection

Dependencies

• Core: Python 3.13+, PyTorch, Pandas, Scikit-learn
• RL Framework: Gymnasium for environment interface
• API: FastAPI, Uvicorn, Pydantic for web services
• Monitoring: Scapy, Psutil for network analysis
• Utilities: Loguru, Typer, Tqdm for enhanced functionality

Initial Release Features

• Complete intrusion detection system
• Pre-trained models for immediate use
• Comprehensive API for integration
• Real-time monitoring capabilities
• Extensive documentation and examples

Development History

Project Inception

The RL-IDS project was initiated to address the need for adaptive intrusion detection systems that can learn and evolve with changing threat landscapes. Traditional signature-based systems often fail to detect novel attacks, while rule-based systems require constant manual updates.

Technology Choices

• Reinforcement Learning: Chosen for its ability to adapt and learn from feedback
• Deep Q-Networks: Selected for their proven effectiveness in decision-making tasks
• CICIDS2017 Dataset: Used as the standard benchmark for network intrusion detection
• FastAPI: Selected for high-performance API development with automatic documentation

Future Roadmap

• Enhanced Model Architectures: Exploration of transformer-based models
• Multi-Agent Systems: Distributed detection across network segments
• Federated Learning: Privacy-preserving collaborative learning
• Real-time Adaptation: Online learning capabilities
• Extended Protocol Support: IPv6, QUIC, and emerging protocols

Contributing

We welcome contributions to RL-IDS! Please see our Contributing Guide for details on: - Development setup and environment - Code style and quality standards - Testing requirements and procedures - Pull request process and guidelines - Issue reporting and feature requests

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• CICIDS2017 Dataset: University of New Brunswick for the comprehensive dataset
• PyTorch Team: For the excellent deep learning framework
• FastAPI: For the modern, high-performance web framework
• Gymnasium: For the standardized RL environment interface
• Open Source Community: For the countless libraries and tools that made this project possible