Decision-Theoretic Incident Response Planning with a Lightweight Large Language Model
A decision-support system powered by a foundation model for recovering from cyberattacks in networked systems.
Requirements
- Python 3.8+
torchtorchvisionnumpytransformerspeftbitsandbytesacceleratehf-transfer
Development Requirements
- Python 3.8+
flake8(for linting)flake8-rst-docstrings(for linting docstrings)tox(for automated testing)pytest(for unit tests)pytest-cov(for unit test coverage)mypy(for static typing)mypy-extensions(for static typing)mypy-protobuf(for static typing)types-PyYaml(for static typing)types-paramiko(for static typing)types-protobuf(for static typing)types-requests(for static typing)types-urllib3(for static typing)sphinx(for API documentation)sphinxcontrib-napoleon(for API documentation)sphinx-rtd-theme(for API documentation)pytest-mock(for mocking tests)pytest-grpc(for grpc tests)
Installation
# install from pip
pip install llm_recovery==<version>
# local install from source
$ pip install -e llm_recovery
# or (equivalently):
make install
# force upgrade deps
$ pip install -e llm_recovery --upgrade
# git clone and install from source
git clone https://github.com/Limmen/llm_recovery
cd llm_recovery
pip3 install -e .
# Install development dependencies
$ pip install -r requirements_dev.txt
Development tools
Install all development tools at once:
make install_dev
or
pip install -r requirements_dev.txt
Static code analysis
To run the Python linter, execute the following command:
flake8 .
# or (equivalently):
make lint
To run the mypy type checker, execute the following command:
mypy .
# or (equivalently):
make types
Unit tests
To run all the unit tests, execute the following command:
pytest
# or (equivalently):
make unit_tests
To run tests of a specific test suite, execute the following command:
pytest -k "ClassName"
To generate a coverage report, execute the following command:
pytest --cov=llm_recovery
Run tests and code analysis in different python environments
To run tests and code analysis in different python environments, execute the following command:
tox
# or (equivalently):
make tests
Create a new release and publish to PyPi
First build the package by executing:
python -m build
# or (equivalently)
make build
After running the command above, the built package is available at ./dist.
Push the built package to PyPi by running:
python -m twine upload dist/*
# or (equivalently)
make push
To run all commands for the release at once, execute:
make release
Author & Maintainer
Kim Hammar kimham@kth.se
Copyright and license
Creative Commons
(C) 2025, Kim Hammar
Metadata
Release files for llm-recovery 0.0.13
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llm_recovery-0.0.13.tar.gz | 25.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llm_recovery-0.0.13-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 47.0 kB
Release files / llm_recovery-0.0.13.tar.gz
| Download URL | llm_recovery-0.0.13.tar.gz |
|---|---|
| Size | 25.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.1.0 CPython/3.11.11
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Release files / llm_recovery-0.0.13-py3-none-any.whl
| Download URL | llm_recovery-0.0.13-py3-none-any.whl |
|---|---|
| Size | 21.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.11.11
|