AIDAS Protocol Implementation

AI-Enhanced Intrusion Detection and Authentication for Autonomous Vehicles

🚗 Overview

AIDAS is a comprehensive security protocol designed for autonomous vehicle ecosystems. It combines cutting-edge technologies including Physical Unclonable Functions (PUF), Chaotic Map Cryptography, and Deep Q-Network (DQN) based intrusion detection to provide robust authentication and security for autonomous vehicles, charging stations, and operators.

✨ Key Features

• 🔐 Multi-layered Security: Hardware-level PUF, cryptographic protocols, and AI-based threat detection
• 🤖 AI-Enhanced Detection: Deep Q-Network for adaptive intrusion detection
• 🔧 Modular Architecture: Clean, extensible codebase with proper separation of concerns
• 📊 Real-time Monitoring: Performance metrics, logging, and visualization
• ⚙️ Configuration Management: Flexible YAML-based configuration system
• 🧪 Comprehensive Testing: Unit, integration, and performance test suites

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• Virtual environment (recommended)

Quick Start

# Clone the repository
git clone <repository-url>
cd "AIDAS Implementation"

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure the system
cp config/config.example.yaml config/config.yaml

# Run the enhanced demo
python demo.py

🚀 Usage

Enhanced Interactive Demo

The new modular demo provides comprehensive feature exploration:

python demo.py

Features include:

1. 🎯 Complete system demonstration
2. ⚙️ Configuration management demo
3. 📝 Enhanced logging showcase
4. 🔧 Advanced PUF functionality
5. 🌀 Chaotic cryptography features
6. 🤖 AI intrusion detection
7. 🔐 Protocol simulation
8. 🚀 Performance evaluation
9. ⚡ Advanced cryptographic features
10. 🔒 Security and error handling

Quick Test

Run a simple functionality test:

python -c "
from aidas import AIDASimulator
import secrets

# Create simulator and entities
simulator = AIDASimulator()
bio_data = secrets.token_bytes(32)
operator = simulator.create_operator('TEST_OP', 'password123', bio_data)
vehicle = simulator.create_vehicle('TEST_AV')
station = simulator.create_charging_station('TEST_CS')

# Run authentication
result = simulator.simulate_authentication_session(
    operator.entity_id, vehicle.entity_id, station.entity_id
)

print(f'Authentication: {\"✅ SUCCESS\" if result[\"success\"] else \"❌ FAILED\"}')
print(f'Latency: {result[\"latency_ms\"]:.2f} ms')
"

📁 Enhanced Project Structure

AIDAS-Implementation/
├── aidas/                      # Main package
│   ├── core/                   # Core components
│   │   ├── entities.py         # Protocol entities
│   │   ├── crypto.py           # Cryptographic engine
│   │   ├── puf.py              # Physical Unclonable Function
│   │   └── chaotic_map.py      # Chaotic map cryptography
│   ├── ai/                     # AI components
│   │   └── dqn_detector.py     # DQN intrusion detection
│   ├── protocol/               # Protocol logic
│   │   ├── authentication.py   # Authentication simulator
│   │   └── session.py          # Session management
│   └── utils/                  # Utilities
│       ├── logger.py           # Enhanced logging
│       └── config.py           # Configuration management
├── config/                     # Configuration files
├── tests/                      # Test suites
├── demo.py                     # Enhanced demo script
├── aidas_protocol.py           # Legacy implementation
└── interactive_demo.py         # Legacy demo

🔧 Core Components

1. PUF Simulator

puf = PUFSimulator("device_id")
challenge = b"random_challenge"
response = puf.generate_response(challenge)

2. Chaotic Map

chaotic_map = ChaoticMap(r=3.99, x0=0.1)
key = chaotic_map.generate_key(32)  # 32-byte key

3. DQN Intrusion Detector

detector = DQNIntrusionDetector()
result = detector.detect_intrusion(network_features)

4. Entity Creation

simulator = AIDASimulator()
operator = simulator.create_operator("OP001", "password", bio_data)
vehicle = simulator.create_vehicle("AV001")
station = simulator.create_charging_station("CS001")

📈 Performance Metrics

Based on the research implementation:

• Detection Accuracy: 97.8%
• False Positive Rate: 1.2%
• Authentication Latency: 6.4ms (average)
• Communication Overhead: 2176 bits
• Computational Overhead Reduction: 31.25%

🛣️ Roadmap

• Core protocol implementation
• Interactive demo
• GUI interface (Issue #1)
• Comprehensive test suite
• REST API
• Docker support
• Production deployment

See CLAUDE.md for detailed implementation plan.

🤝 Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please read CLAUDE.md for detailed development guidelines.

🐛 Issues

Found a bug or have a feature request? Please check existing issues or create a new one.

Current open issues:

• #1 GUI is absent

📚 Documentation

• Implementation Plan - Comprehensive development guide
• Research Paper - Original research

🔒 Security

This implementation includes multiple security layers:

• Hardware-level security (PUF)
• Cryptographic protection (AES-256, ECC-256)
• AI-based threat detection
• Protection against various attacks (MITM, DDoS, Replay, etc.)

📄 License

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

👥 Authors

• Shafiq Ahmed - Initial implementation - shafiqahmeddev

🙏 Acknowledgments

• Based on the research paper "AIDAS: AI-Enhanced Intrusion Detection and Authentication for Autonomous Vehicles"
• Thanks to all contributors and researchers in the field of autonomous vehicle security

📞 Contact

For questions or support, please open an issue or contact the maintainers.

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Note: This is a research implementation. For production use, additional security auditing and testing is recommended.