Ledger's Advanced Side Channel Analysis Repository

A fast, versatile, and open source python3 library designed to facilitate Side-Channel Analysis.

lascar is intended to be used by seasoned side-channel attackers as well as laymen who would like to get a feel of side-channel analysis.

From side-channel acquisitions to results management, passing by signal synchronisation, custom attacks, lascar provides classes/functions to solve most of the obstacles an attacker would face, when needed to perform sound, state-of-the-art side-channel analysis.

This project has been developed in parallel of the activities done by Ledger Donjon (Ledger's security team), to fully match our needs regarding side-channel evaluation.

The philosophy behind lascar is to simplify for the end user the process of a side-channel analysis. It provides many classes and functions that you can accomodate with, or inherit from to do the job you need.

• Openness: lascar library is open source and is intended to facilitate attack implementations, and exchange between users. Contributing to lascar is strongly encouraged.
• Simplicity: For basic state of the art attacks, the corresponding lascar script shall stay basic
• Compatibility: Since lascar relies on mainstream python libraries (numpy, sklearn, keras): lascar is easily deployable
• Flexibility: Implement your own classes (for your already existing trace format, your specific attacks, the way you want your output to be...), use different languages (provided that you bind them with python),...

Please note that performance has not yet been challenged.

The tutorial/examples folders of the library provide basic scripts solving the most frequent use-cases of side-channel analysis.

Clone the repository then use the setup.py file, based on setuptools:

python3 setup.py install --user

Build the doc:

cd docs/
make html

This library requires the following packages:

• numpy
• scipy
• matplotlib: for curve visualization
• vispy: for curve visualization
• sklearn: for machine learning
• keras: for deep learning
• tensorflow: keras backend
• h5py: for data storage
• progressbar2
• pytest
• numba

The tutorial folder contains commented scripts to understand how to handle the core classes behind lascar (Container, Session, Engine, OutputMethod)

• 01-discovering-containers.py
• 02-store_containers.py
• 03-abstract-container.py
• 04-acquisition-setup-example.py
• 05-synchronization-example.py
• 06-session-introduction.py
• 07-session-dpa-example.py
• 08-session-manage-outputs.py

See examples.md

Use lascarctl, lascar command line tool. For documentation, see here

Created in 2018, Ledger Donjon (Ledger security team) regroups experts in security with a wide range of expertise (such as software, perturbation and side-channel attacks, secured development, reverse engineering, ...). Based in Paris, Ledger's Donjon tends to shift the paradigm of security through obscurity. Take a look at our blogposts and the detailed introduction!

Ledger's Donjon would like to thank the people behind ASCAD, for making available real side-channel traces and scripts for analysis. Their traces have been used in examples/ascad/ folder to illustrate how to use lascar to reproduce (part of) their study.