DGA-FANCI: Random Forest for DGA Detection

Random Forest with 27 FANCI features, trained on 54 DGA families. Part of the DGA Multi-Family Benchmark (Reynier et al., 2026).

Note: Model file is ~1 GB. First download in Colab will take 1-2 minutes.

Usage

from huggingface_hub import hf_hub_download
import importlib.util

model_path = hf_hub_download("Reynier/dga-fanci", "fanci_dga_detector.joblib")
model_py = hf_hub_download("Reynier/dga-fanci", "model.py")

spec = importlib.util.spec_from_file_location("fanci_model", model_py)
mod = importlib.util.module_from_spec(spec)
spec.loader.exec_module(mod)

model = mod.load_model(model_path)
results = mod.predict(model, ["google.com", "xkr3f9mq.ru"])
print(results)

Citation

@article{reynier2026dga,
  title={DGA Multi-Family Benchmark: Comparing Classical and Transformer-based Detectors},
  author={Reynier et al.},
  year={2026}
}