scaaml 2.0.2

Side Channel Attack Assisted with Machine Learning

SCAAML: Side Channel Attacks Assisted with Machine Learning

SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framework dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x.

Latest Updates

• Sep 2024: GPAM the first power side-channel general model capable of attacking multiple algorithms using full traces, were presented at CHES and are now available for download.

• Sep 2024: ECC datasets our large-scale ECC datasets are available for download.

Available components

• scaaml/: The SCAAML framework code. Its used by the various tools.

• scaaml_intro/: A Hacker Guide To Deep Learning Based Side Channel Attacks. Code, dataset and models used in our step by step tutorial on how to use deep-learning to perform AES side-channel attacks in practice.

• GPAM Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning model and datasets needed to reproduce our results are available for download.

• ECC datasets A collection of large-scale hardware protected ECC datasets.

Install

Dependencies

To use SCAAML you need to have a working version of TensorFlow 2.x and a version of Python >=3.9

SCAAML framework install

1. Clone the repository: git clone github.com/google/scaaml/
2. Create and activate Python virtual environment: python3 -m venv my_env source my_env/bin/activate
3. Install dependencies: python3 -m pip install --require-hashes -r requirements.txt
4. Install the SCAAML package: python setup.py develop

Publications & Citation

Here is the list of publications and talks related to SCAAML. If you use any of its codebase, models or datasets please cite the repo and the relevant papers:

@software{scaaml_2019,
    title = {{SCAAML: Side Channel Attacks Assisted with Machine Learning}},
    author={Bursztein, Elie and Invernizzi, Luca and Kr{\'a}l, Karel and Picod, Jean-Michel},
    url = {https://github.com/google/scaaml},
    version = {1.0.0},
    year = {2019}
}

Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning

@article{bursztein2023generic,
  title={Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning},
  author={Bursztein, Elie and Invernizzi, Luca and Kr{\'a}l, Karel and Moghimi, Daniel and Picod, Jean-Michel and Zhang, Marina},
  journal={CHES},
  year={2024}
}

SCAAML AES tutorial

DEF CON talk that provides a practical introduction to AES deep-learning based side-channel attacks

@inproceedings{burszteindc27,
title={A Hacker Guide To Deep Learning Based Side Channel Attacks},
author={Elie Bursztein and Jean-Michel Picod},
booktitle ={DEF CON 27},
howpublished = {\url{https://elie.net/talk/a-hackerguide-to-deep-learning-based-side-channel-attacks/}}
year={2019},
editor={DEF CON}
}

Disclaimer

This is not an official Google product.