Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/eshard/scared
Make your first side-channel attack on public datasets with eShard. This is a mirror of scared Gitlab repository. All contributions and merge request must be done through Gitlab project.
https://github.com/eshard/scared
cryptography python side-channel side-channel-channel-attack traces
Last synced: 22 days ago
JSON representation
Make your first side-channel attack on public datasets with eShard. This is a mirror of scared Gitlab repository. All contributions and merge request must be done through Gitlab project.
- Host: GitHub
- URL: https://github.com/eshard/scared
- Owner: eshard
- License: agpl-3.0
- Created: 2019-06-14T14:14:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-01-16T13:47:25.000Z (almost 2 years ago)
- Last Synced: 2024-08-05T17:45:00.225Z (4 months ago)
- Topics: cryptography, python, side-channel, side-channel-channel-attack, traces
- Language: Python
- Homepage: https://www.gitlab.com/eshard/scared
- Size: 42.1 MB
- Stars: 78
- Watchers: 12
- Forks: 20
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-hacking-lists - eshard/scared - Make your first side-channel attack on public datasets with eShard. This is a mirror of scared Gitlab repository. All contributions and merge request must be done through Gitlab project. (Python)
README
# SCAred
[](https://gitlab.com/eshard/scared/commits/master)
[](https://pypi.org/project/scared/)
[](https://anaconda.org/eshard/scared)
[](https://anaconda.org/eshard/scared)
scared is a side-channel analysis framework maintained by [eShard](http://www.eshard.com) team.
## Getting started
### Requirements
Scared need python **3.6**, **3.7**, **3.8** or **3.9**.
You can install `scared`, depending on your setup:
- from source
- with `pip`
- with `conda`
#### Install with `conda`
You just have to run:
```bash
conda install -c eshard scared
```
#### Install with `pip`
Python wheels are available from Pypi, just run:
```bash
pip install scared
```
#### Install from sources
To install from sources, you will need to run:
```bash
pip install .
```
from the source folder.
If you are planning to contribute, see [CONTRIBUTING.md](CONTRIBUTING.md) to install the library in development mode and run the test suite.
### Make a first cool thing
Start using scared by doing a cool thing:
```python
# First import the lib
import scared
import numpy as np
# Define a selection function
@scared.attack_selection_function
def first_add_key(plaintext, guesses):
res = np.empty((plaintext.shape[0], len(guesses), plaintext.shape[1]), dtype='uint8')
for i, guess in enumerate(guesses):
res[:, i, :] = np.bitwise_xor(plaintext, guess)
return res
# Create an analysis CPA
a = scared.CPAAttack(
selection_function=first_add_key,
model=scared.HammingWeight(),
discriminant=scared.maxabs)
# Load some traces, for example a dpa v2 subset
ths = scared.traces.read_ths_from_ets_file('dpa_v2.ets')
# Create a container for your ths
container = scared.Container(ths)
# Run!
a.run(container)
```
## Documentation
To go further and learn all about scared, please go to [the full documentation](https://eshard.gitlab.io/scared).
You can also have an interactive introduction to scared by launching these [notebooks with Binder](https://mybinder.org/v2/gl/eshard%2Fscared-notebooks/master).
## Contributing
All contributions, starting with feedbacks, are welcomed.
Please read [CONTRIBUTING.md](CONTRIBUTING.md) if you wish to contribute to the project.
## License
This library is licensed under LGPL V3 license. See the [LICENSE](LICENSE) file for details.
It is mainly intended for non-commercial use, by academics, students or professional willing to learn the basics of side-channel analysis.
If you wish to use this library in a commercial or industrial context, [eShard](https://www.eshard.com) provides commercial licenses under fees. [Contact us](mailto:[email protected])!
## Authors
See [AUTHORS](AUTHORS.md) for the list of contributors to the project.
## Binary builds available
Binary builds (wheels on pypi and conda builds) are available for the following platforms and Python version.
Platforms:
- Linux x86 64
- Macosx x86 64
Python version:
- 3.6
- 3.7
- 3.8