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A Cyberattack Warning System for Enhancing Connected Vehicle Safety Under Spoofing Cyberattacks: A Generative-Based Human-in-the-Loop Trajectory Prediction Approach

This repository contains the research code used for experiments on traffic/vehicle trajectory analysis and forecasting. The project has been lightly refactored to make core utilities reusable and easier to run while keeping the original experiment scripts for reference.

Contents

  • src/caca/ — refactored library code (preprocessing, GAN, training helpers).
  • scripts/ — small CLI wrappers for common tasks (preprocessing, legacy runners).
  • tests/ — unit tests and lightweight smoke tests suitable for CI.
  • legacy top-level scripts — original experiment notebooks and scripts (left for reproducibility).

Requirements

  • Python 3.8+ (use a virtual environment)
  • See requirements.txt for the Python dependencies used for tests and examples.

Quick start

  1. Create and activate a virtual environment, then install dependencies:
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
  1. Run the preprocessing example (adjust input/output paths):
python scripts/run_preprocessing.py --input path/to/your.csv --output preprocessed.csv

Testing

  • Run the unit tests with pytest:
pip install -r requirements.txt
pytest -q

Best practices

  • Do not commit large datasets, trained model weights, or logs to the repository — store them externally or use Git LFS.
  • Prefer importing functions from src/caca/ rather than executing legacy scripts directly.
  • Add focused unit tests when refactoring legacy scripts to preserve behavior.

Related paper (recommended reading)

How to cite this repository

  • If you use the code in this repository, please cite the repository. Example BibTeX entry:
@misc{A-Cyberattack-Warning-System-2026,
	title = {A-Cyberattack-Warning-System},
	author = {YingfanGu},
	year = {2026},
	howpublished = {GitHub repository},
	url = {https://github.com/YingfanGu/A-Cyberattack-Warning-System}
}

License

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

Contributing

  • Contributions are welcome. Please open an issue or a pull request with a concise description of the change.

Contact

  • For questions about the code or the experiments, open an issue or contact the maintainer via the repository.