SCAAML: Side Channel Attacks Assisted with Machine Learning
SCAAML (Side Channel Attacks Assisted with Machine Learning) is a deep learning framwork dedicated to side-channel attacks. It is written in python and run on top of TensorFlow 2.x.
Available compoments
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.
Install
Dependencies
To use SCAAML you need to have a working version of TensorFlow 2.x and a version of Python >=3.6
SCAAML framework install
- Clone the repository:
git clone github.com/google/scaaml/
- Install the SCAAML package:
python setup.py develop
Dataset and models
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.
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:
@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
SCAAML AES tutorial
DEF CON talk that provides a practical introduction to AES deep-learning based side-channel attacks
@inproceedings{burzteindc27,
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.