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README.md

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+---
+language: en
+tags:
+  - dga
+  - cybersecurity
+  - domain-generation-algorithm
+  - text-classification
+  - keras
+license: mit
+---
+
+# DGA-LABin: BiLSTM + Attention (Keras) for DGA Detection
+
+Bidirectional LSTM with Weighted Self-Attention (Keras) trained on 54 DGA families.
+Part of the **DGA Multi-Family Benchmark** (Reynier et al., 2026).
+
+## Model Description
+
+- **Architecture:** Embedding(128) → BiLSTM(128) → SeqWeightedAttention → Dense → sigmoid
+- **Input:** Character-level encoding, left-padded to 64 chars
+- **Output:** Binary classification — `legit` (0) or `dga` (1)
+- **Framework:** Keras (TensorFlow)
+
+## Usage
+
+```python
+# Install dependency
+# !pip install keras-self-attention
+
+from huggingface_hub import hf_hub_download
+import importlib.util
+
+weights = hf_hub_download("Reynier/dga-labin", "LABin_best_model.keras")
+model_py = hf_hub_download("Reynier/dga-labin", "model.py")
+
+spec = importlib.util.spec_from_file_location("labin_model", model_py)
+mod = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(mod)
+
+model = mod.load_model(weights)
+results = mod.predict(model, ["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}
+}
+```