Cybersecurity Framework for IoT-Enabled Autonomous Vehicles

Project Overview

This project aims to develop a simplified prototype of a cybersecurity framework designed to secure IoT-enabled autonomous vehicles. The prototype integrates machine learning for anomaly detection and blockchain technology to ensure data integrity and secure communication. This framework is tested in a simulated environment to evaluate its effectiveness in mitigating cyber threats.

Project Structure

cybersecurity_framework_project/
├── data/
│   ├── raw_data/
│   │   └── simulated_iot_data.csv
│   ├── processed_data/
│   │   ├── training_data.csv
│   │   ├── testing_data.csv
│   │   └── anomaly_injected_data.csv
│   └── blockchain_logs/
│       └── blockchain_data.json
├── models/
│   ├── anomaly_detection_model.pkl
│   └── trained_model/
│       ├── isolation_forest.pkl
│       └── ...
├── web_app/
│   ├── templates/
│   │   ├── index.html
│   │   └── dashboard.html
│   ├── static/
│   │   ├── css/
│   │   └── js/
│   ├── app.py
│   └── requirements.txt
├── notebooks/
│   ├── data_preprocessing.ipynb
│   ├── model_training.ipynb
│   ├── blockchain_implementation.ipynb
│   └── data_analysis_and_visualization.ipynb
├── tests/
│   ├── test_anomaly_detection.py
│   ├── test_blockchain.py
│   └── ...
└── README.md

Getting Started

Prerequisites

• Python 3.8+
• Pip (Python package manager)
• Virtual environment setup (optional but recommended)

Setup Instructions

1. Clone the Repository:

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

2. Create a Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Dependencies: Navigate to the web_app/ directory and install the required Python packages:

   pip install -r requirements.txt

4. Data Preprocessing: Use the Jupyter notebooks in the notebooks/ directory to preprocess the data and train the machine learning model:

   • data_preprocessing.ipynb: Clean and preprocess the IoT data.
   • model_training.ipynb: Train the anomaly detection model.
   • blockchain_implementation.ipynb: Implement the blockchain for data integrity.
   • data_analysis_and_visualization.ipynb: Analyze and visualize the results.

5. Run the Web Application: Navigate to the web_app/ directory and run the Flask application:

   python app.py

   Open your web browser and go to http://localhost:5000 to access the dashboard.

6. Testing: Execute the test scripts located in the tests/ directory to ensure everything is functioning correctly:

   pytest tests/

Features

• Anomaly Detection: Machine learning algorithms are used to detect anomalies in IoT sensor data, identifying potential cyber threats.
• Blockchain Integration: Blockchain technology ensures the integrity of the data and secures communication within the IoT network.
• Web Dashboard: A simple web interface displays real-time data, anomalies, and blockchain logs, allowing for easy monitoring and analysis.

Usage

1. Data Monitoring: The web dashboard allows you to monitor IoT sensor data and detect anomalies in real-time.
2. Anomaly Injection: You can simulate cyber-attacks by injecting anomalies into the data and observe how the system responds.
3. Blockchain Validation: The blockchain ensures that all data transactions are secure and immutable, providing a tamper-proof record of events.

Testing and Validation

• Anomaly Detection: The system's accuracy in detecting anomalies can be validated using the test data in the tests/ directory.
• Blockchain Integrity: The blockchain implementation ensures that any tampering with the data is detected, with logs stored in blockchain_logs/.

Future Work

• Expand Simulation Environments: Integrate more advanced simulation platforms to mimic real-world conditions more accurately.
• Enhanced Machine Learning Models: Incorporate more sophisticated models and algorithms for improved anomaly detection.
• Scalability: Test the framework's scalability with larger datasets and more complex IoT networks.

Contributing

If you have suggestions or improvements, feel free to submit a pull request or open an issue. Contributions are always welcome!

License

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

---

This README provides a comprehensive overview of the prototype, guiding users through setup, usage, and further development possibilities.