Here is a short release note for your repository containing the provided notebook:
Release Notes
Version 1.0.0
New Features
- Machine Learning-Based Classification of Hardware Trojans:
- Implemented classification models for detecting hardware Trojans in FPGAs implementing RISC-V cores.
- Utilized various machine learning algorithms to enhance detection accuracy.
- Included comprehensive data preprocessing steps and feature engineering techniques.
Notebook Highlights
- ht_classification.ipynb:
- Detailed exploration and implementation of machine learning techniques for classifying hardware Trojans.
- Step-by-step guide on data loading, preprocessing, model training, and evaluation.
- Visualizations and analysis to interpret model performance and results.
References
- Added citation for the related conference paper:
@inproceedings{ribes2024machine,
author={Stefano Ribes. and Fabio Malatesta. and Grazia Garzo. and Alessandro Palumbo.},
title={Machine Learning-Based Classification of Hardware Trojans in FPGAs Implementing RISC-V Cores},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - ICISSP},
year={2024},
pages={717-724},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012324200003648},
isbn={978-989-758-683-5},
issn={2184-4356},
}
Improvements
- Enhanced documentation and comments within the notebook to improve readability and usability.
Known Issues
- None at this time.
This release provides a solid foundation for further enhancements and development in the classification of hardware Trojans using machine learning.