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README.md

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+---
+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}
+}
+```