SCAAML: Side Channel Attacks Assisted with Machine Learning

Documentation

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:

bibtex @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

bibtex @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

bibtex @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.