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--- |
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license: mit |
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library_name: pytorch |
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tags: |
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- keystroke-dynamics |
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- lstm |
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- cybersecurity |
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- hid |
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--- |
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# LSTM-64win-Keystrokes |
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## Summary |
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This repository contains a PyTorch **LSTM** classifier for **Human vs HID** keystroke control detection using **windowed** timing features. |
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The label for each window is the **last keystroke** label in that window. |
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## Training setup (as implemented) |
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- **Window size:** 64 |
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- **Stride:** 1 |
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- **Label mapping:** {"human": 0, "hid": 1} |
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- **Window label:** last-keystroke in the window |
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- **Features (F=10):** |
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- `duration` |
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- `DD_time` |
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- `DU_time` |
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- `UD_time` |
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- `UU_time` |
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- `run_avg_duration` |
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- `run_avg_DD` |
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- `run_avg_DU` |
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- `run_avg_UD` |
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- `run_avg_UU` |
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### Preprocessing |
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- Windows built with NumPy `sliding_window_view`. |
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- Standardization via `StandardScaler` fitted on **training windows only**, across all timesteps and samples. |
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### Model |
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- `torch.nn.LSTM` (unidirectional, batch_first) |
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- Hidden size: 64 |
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- Num layers: 1 |
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- Dropout: 0.0 |
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- Head: Linear(hidden_size → 2) |
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### Optimization |
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- Optimizer: Adam |
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- LR: 0.001 |
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- Batch size: 256 |
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- Epochs: 30 |
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- Seed: 42 |
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## Files |
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- `model.safetensors`: model weights |
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- `config.json`: architecture + feature metadata |
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- `scaler.joblib`: fitted StandardScaler |
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- `metrics.json`: classification report + confusion matrix |
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- `inference.py`: minimal loading + prediction example |
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## Usage (minimal) |
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```python |
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from inference import load_model_and_scaler, predict_df |
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model, scaler, cfg = load_model_and_scaler("NourFakih/LSTM-64win-Keystrokes") |
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y_pred = predict_df(df, model, scaler, cfg) # df must contain cfg["feature_cols"] |
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``` |
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