Guidelines for DGA-based Malware Detection

In this repository, we provide the code to reproduce the results in the "Down to earth! Guidelines for DGA-based Malware Detection" paper.

Repository structure

This repository is organized as follows:

src/
    |- dga_analysis/                        # Library containing models and benchmarks
      |- datasets/                          # Dataloaders for benign and DGA datasets
      |- detection/                         # Reference detection models
      |- generators/                        # Custom generators models
      |- utils/                             # Statistical and benchmarking tools
experiments/                                # Scripts and notebooks for generating the results in the paper
tests/                                      # Library unit tests

Install

pip install -e .
pip install -e .[testing] # for the development setup

Get the datasets

Review the docs here and create the local datasets

Run the tests

If everything is fine, most of the tests should pass

pytest -vvsx

Citing

If you use this code, please cite the associated paper:

@inproceedings{cebere2024guidelines,
  title={Down to earth! Guidelines for DGA-based Malware Detection},
  author={Cebere, Bogdan and Flueren, Jonathan and Sebastián, Silvia and Plohmann, Daniel and Rossow, Christian},
  booktitle={Proceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses},
  year={2024}
}