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.

• 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.

To use SCAAML you need to have a working version of TensorFlow 2.x and a version of Python >=3.6

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

Make sure to have: sudo apt install python3 python3-pip python3-venv and activated the virtual environment.

Install requirements: pip install --require-hashes -r base-tooling-requirements.txt

Update: pip-compile --allow-unsafe requirements.in --generate-hashes --upgrade and commit requirements.txt.

Every SCAAML component rely on a datasets and optional models that you will need to download in the component directory. The link to download those are available in the components specific README.md. Simply click on the directory representing the component of your choice, or the link to the component in the list above.

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

@online{bursztein2019scaaml,
  title={SCAAML:  Side Channel Attacks Assisted with Machine Learning},
  author={Bursztein, Elie and others},
  year={2019},
  publisher={GitHub},
  url={https://github.com/google/scaaml},
}

Additionally please also cite the talks and publications that are the most relevant to your work, so reader can quickly find the right information. Last but not least, you are more than welcome to add your publication/talk to the list below by making a pull request 😊.

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}
}

This is not an official Google product.