dga-bilstm

DGA-BiLSTM: Bidirectional LSTM + Self-Attention for DGA Detection

BiLSTM with Self-Attention (Namgung et al. 2021) trained on 54 DGA families. Part of the DGA Multi-Family Benchmark (Reynier et al., 2026).

Model Description

• Architecture: Embedding → BiLSTM(128×2) → Self-Attention → FC(64) → sigmoid
• Input: Character-level encoding, right-padded to 75 chars
• Output: Binary classification — legit (0) or dga (1)
• Framework: PyTorch
• Reference: Namgung et al., Security and Communication Networks, 2021

Performance (54 DGA families, 30 runs each)

Usage

from huggingface_hub import hf_hub_download
import importlib.util, torch

weights = hf_hub_download("Reynier/dga-bilstm", "bilstm_best.pth")
model_py = hf_hub_download("Reynier/dga-bilstm", "model.py")

spec = importlib.util.spec_from_file_location("bilstm_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

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