| Name | scared JSON |
| Version |
1.1.4
JSON |
| download |
| home_page | https://gitlab.com/eshard/scared |
| Summary | scared is a side-channel analysis framework. |
| upload_time | 2024-08-13 10:35:25 |
| maintainer | None |
| docs_url | None |
| author | eshard |
| requires_python | <3.13,>=3.7.0 |
| license | None |
| keywords |
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| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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 **3.7**, **3.8**, **3.9** or **3.10**.
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:scared@eshard.com)!
## 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
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"description": "# SCAred\n\n[](https://gitlab.com/eshard/scared/commits/master)\n[](https://pypi.org/project/scared/)\n[](https://anaconda.org/eshard/scared)\n[](https://anaconda.org/eshard/scared)\n\nscared is a side-channel analysis framework maintained by [eShard](http://www.eshard.com) team.\n\n## Getting started\n\n### Requirements\n\nScared need python **3.7**, **3.8**, **3.9** or **3.10**.\n\nYou can install `scared`, depending on your setup:\n\n- from source\n- with `pip`\n- with `conda`\n\n#### Install with `conda`\n\nYou just have to run:\n\n```bash\nconda install -c eshard scared\n```\n\n#### Install with `pip`\n\nPython wheels are available from Pypi, just run:\n\n```bash\npip install scared\n```\n\n#### Install from sources\n\nTo install from sources, you will need to run:\n\n```bash\npip install .\n```\n\nfrom the source folder.\n\nIf you are planning to contribute, see [CONTRIBUTING.md](CONTRIBUTING.md) to install the library in development mode and run the test suite.\n\n### Make a first cool thing\n\nStart using scared by doing a cool thing:\n\n```python\n# First import the lib\nimport scared\nimport numpy as np\n\n# Define a selection function\n@scared.attack_selection_function\ndef first_add_key(plaintext, guesses):\n res = np.empty((plaintext.shape[0], len(guesses), plaintext.shape[1]), dtype='uint8')\n for i, guess in enumerate(guesses):\n res[:, i, :] = np.bitwise_xor(plaintext, guess)\n return res\n\n# Create an analysis CPA\na = scared.CPAAttack(\n selection_function=first_add_key,\n model=scared.HammingWeight(),\n discriminant=scared.maxabs)\n\n# Load some traces, for example a dpa v2 subset\nths = scared.traces.read_ths_from_ets_file('dpa_v2.ets')\n\n# Create a container for your ths\ncontainer = scared.Container(ths)\n\n# Run!\na.run(container)\n```\n\n## Documentation\n\nTo go further and learn all about scared, please go to [the full documentation](https://eshard.gitlab.io/scared).\nYou can also have an interactive introduction to scared by launching these [notebooks with Binder](https://mybinder.org/v2/gl/eshard%2Fscared-notebooks/master).\n\n## Contributing\n\nAll contributions, starting with feedbacks, are welcomed.\nPlease read [CONTRIBUTING.md](CONTRIBUTING.md) if you wish to contribute to the project.\n\n## License\n\nThis library is licensed under LGPL V3 license. See the [LICENSE](LICENSE) file for details.\n\nIt is mainly intended for non-commercial use, by academics, students or professional willing to learn the basics of side-channel analysis.\n\nIf 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:scared@eshard.com)!\n\n## Authors\n\nSee [AUTHORS](AUTHORS.md) for the list of contributors to the project.\n\n## Binary builds available\n\nBinary builds (wheels on pypi and conda builds) are available for the following platforms and Python version.\n\nPlatforms:\n\n- Linux x86 64\n- Macosx x86 64\n\nPython version:\n\n- 3.6\n- 3.7\n- 3.8\n",
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