https://github.com/pralab/secml_malware
Create adversarial attacks against machine learning Windows malware detectors
https://github.com/pralab/secml_malware
adversarial-machine-learning attack infosec machine-learning python security
Last synced: 12 days ago
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Create adversarial attacks against machine learning Windows malware detectors
- Host: GitHub
- URL: https://github.com/pralab/secml_malware
- Owner: pralab
- License: mit
- Created: 2019-12-19T09:41:53.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2025-09-30T13:26:56.000Z (4 months ago)
- Last Synced: 2025-11-22T21:06:04.483Z (2 months ago)
- Topics: adversarial-machine-learning, attack, infosec, machine-learning, python, security
- Language: Python
- Homepage: https://secml-malware.readthedocs.io/
- Size: 4.65 MB
- Stars: 241
- Watchers: 7
- Forks: 55
- Open Issues: 13
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-executable-packing - SecML Malware - Create adversarial attacks against machine learning Windows malware detectors. (:wrench: Tools / Before 2000)
README
# SecML Malware
[](https://secml-malware.readthedocs.io/en/latest/?badge=latest)
Python library for creating adversarial attacks against Windows Malware detectors.
Built on top of [SecML](https://secml.gitlab.io), SecML Malware includes most of the attack proposed in the state of the art.
We include a [pre-trained MalConv](https://github.com/endgameinc/ember) model trained by EndGame, used for testing.
# Included Attacks
* **Partial DOS Header manipulation**, formulated by [Demetrio et al.](https://arxiv.org/abs/1901.03583)
* **Padding attack**, formulated by [Kolosnjaji et al.](http://pralab.diee.unica.it/sites/default/files/kolosnjaji18-eusipco.pdf)
* **GAMMA**, formulated by [Demetrio et al.](https://arxiv.org/abs/2003.13526)
* **FGSM padding + slack** formulated by [Kreuk et al.](https://arxiv.org/abs/1802.04528) and [Suciu et al.](https://arxiv.org/abs/1810.08280)
* **Content shifting and DOS header extension** formulated by [Demetrio et al.](https://arxiv.org/pdf/2008.07125.pdf)
* **Header Fields** inspired by [Nisi et al.](https://www.s3.eurecom.fr/~darion/papers/raid2021.pdf)
# Installation
Navigate to the folder where you want to clone the project.
I recommend creating a new environment (I use `conda`):
```bash
conda create -n secml_malware_env python=3.9
conda activate secml_malware_env
pip install git+https://github.com/zangobot/ember.git
pip install secml-malware
```
[//]: # (You also need to install `pytorch`, [find instructions here](https://pytorch.org/get-started/locally/). )
[//]: # (Also, you might neet to install *libmagic as well*. [Follow these instructions](https://github.com/ahupp/python-magic#installation) to install properly libmagic.)
If you are an Apple Silicon user, please install *lightgbm* from conda:
```bash
conda install -c conda-forge lightgbm
```
## Optional - Nevergrad
If you want speed up blackbox attacks, you can install [Nevergrad](https://github.com/facebookresearch/nevergrad).
See the blackbox tutorial for more information on its usage.
# How to use
Activate your environment, and import the `secml_malware` package inside your script:
```python
import secml_malware
print(secml_malware.__version__)
```
The tests included in this project show how the library can be used for applying the manipulation to the input programs.
There is also an [example Jupyter notebook tutorial](https://github.com/zangobot/secml_malware/blob/master/attack_tutorial.ipynb) that shows how to build a apply a standard attack.
# Docker
There is also a `Dockerfile` that can be used to start a container and test the library without messing with virtual environments!
```bash
docker build --tag secml_malware:0.3.2 .
docker run --rm -it secml_malware:0.3.2 bash
```
The container is also shipped with `ipython`, for a more interactive experience with this library.
# Cite
If you use our library, please cite us!
```bibtex
@misc{demetrio2021secmlmalware,
title={secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers},
author={Luca Demetrio and Battista Biggio},
year={2021},
eprint={2104.12848},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
```
Also, depending on the manipulations / formalization you are using, please cite our work:
**Content shifting and DOS header extension manipulations** or **RAMEn formalization**
```bibtex
@article{demetrio2021adversarial,
title={Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection},
author={Luca Demetrio and Scott E. Coull and Battista Biggio and Giovanni Lagorio and Alessandro Armando and Fabio Roli},
journal={ACM Transactions on Privacy and Security},
year={2021},
publisher={ACM}
}
```
**GAMMA**
```bibtex
@article{demetrio2021functionality,
title={Functionality-preserving black-box optimization of adversarial windows malware},
author={Demetrio, Luca and Biggio, Battista and Lagorio, Giovanni and Roli, Fabio and Armando, Alessandro},
journal={IEEE Transactions on Information Forensics and Security},
year={2021},
publisher={IEEE}
}
```
**Partial DOS manipulation**
```bibtex
@inproceedings{demetrio2019explaining,
title={Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries},
author={Luca Demetrio and Battista Biggio and Giovanni Lagorio and Fabio Roli and Alessandro Alessandro},
booktitle={ITASEC19},
volume={2315},
year={2019}
}
```
# Bug reports
If you encounter something strange, feel free to open an issue! I am working a lot, and bugs are present everywhere.
Let me know, and I'll try to fix them as soon as possible.
# Testing
I provide a small test suite for the attacks I have developed inside the plugin.
If you want to run them, **ADD GOODWARE/MALWARE samples!**
There are two distinct folders:
```
secml_malware/data/goodware_samples
secml_malware/data/malware_samples/test_folder
```
Please, add samples to both folders (**if and only if** you want to run the internal tests).
# Fundings
The maintainance of this project has been partially by the project “SAFER “Robust Models for Safe and Secure AI Systems” (PE0000013).
