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README.md

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+---
+language: en
+tags:
+  - dga
+  - cybersecurity
+  - domain-generation-algorithm
+  - text-classification
+  - pytorch
+license: mit
+metrics:
+  - accuracy
+  - f1
+---
+
+# DGA-CNN: Character-level CNN for DGA Detection
+
+Character-level Convolutional Neural Network trained to detect Domain Generation Algorithm (DGA) domains.
+Part of the **DGA Multi-Family Benchmark** (Reynier et al., 2026).
+
+## Model Description
+
+- **Architecture:** Single Conv1d layer (64 filters, kernel=3) + MaxPool + FC
+- **Input:** Character-level encoding of domain name (max 75 chars)
+- **Output:** Binary classification — `legit` (0) or `dga` (1)
+- **Framework:** PyTorch
+
+## Performance (54 DGA families, 30 runs each)
+
+| Metric    | Value  |
+|-----------|--------|
+| Accuracy  | 0.9200 |
+| F1        | 0.9000 |
+| Precision | 0.9400 |
+| Recall    | 0.8900 |
+| FPR       | 0.0400 |
+| Query Time| 0.490 ms/domain (CPU) |
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import importlib.util, torch
+
+# Download model files
+weights = hf_hub_download("Reynier/dga-cnn", "dga_cnn_model_1M.pth")
+model_py = hf_hub_download("Reynier/dga-cnn", "model.py")
+
+# Load module
+spec = importlib.util.spec_from_file_location("cnn_model", model_py)
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+
+# Load model
+model = mod.load_model(weights)
+
+# Predict
+results = mod.predict(model, ["google.com", "xkr3f9mq.ru"])
+print(results)
+# [{"domain": "google.com", "label": "legit", "score": 0.02},
+#  {"domain": "xkr3f9mq.ru", "label": "dga", "score": 0.98}]
+```
+
+## Training Data
+
+Trained on `train_1M.csv` — ~845K samples across 54 DGA families + legitimate domains.
+
+## Citation
+
+```bibtex
+@article{reynier2026dga,
+  title={DGA Multi-Family Benchmark: Comparing Classical and Transformer-based Detectors},
+  author={Reynier et al.},
+  year={2026}
+}
+```