---
language: en
tags:
  - dga
  - cybersecurity
  - domain-generation-algorithm
  - text-classification
  - sklearn
license: mit
---

# DGA-Logit: TF-IDF + Logistic Regression for DGA Detection

TF-IDF character n-grams combined with 15 lexical features and Logistic Regression, trained on 54 DGA families.
Part of the **DGA Multi-Family Benchmark** (Reynier et al., 2026).

## Model Description

- **Architecture:** TF-IDF (char n-grams) + 15 lexical features → StandardScaler → Logistic Regression
- **Features:** Length, entropy, digit/vowel ratios, consecutive runs, SLD length, etc.
- **Framework:** scikit-learn
- **File size:** ~8 MB

## Performance (54 DGA families, 30 runs each)

| Metric    | Value  |
|-----------|--------|
| Accuracy  | 0.9277 |
| F1        | 0.9028 |
| Precision | 0.9407 |
| Recall    | 0.8921 |
| FPR       | 0.0367 |
| Query Time| 0.291 ms/domain (CPU) |

## Usage

```python
from huggingface_hub import hf_hub_download
import importlib.util

artifacts_path = hf_hub_download("Reynier/dga-logit", "artifacts.joblib")
model_py = hf_hub_download("Reynier/dga-logit", "model.py")

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

artifacts = mod.load_model(artifacts_path)
results = mod.predict(artifacts, ["google.com", "xkr3f9mq.ru"])
print(results)
```

## Citation

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