LSTM-32win-Keystrokes

LSTM-32win-Keystrokes

Summary

This repository contains a PyTorch LSTM classifier for Human vs HID keystroke control detection using windowed timing features. The label for each window is the last keystroke label in that window.

Training setup (as implemented)

• Window size: 32
• Stride: 1
• Label mapping: {"human": 0, "hid": 1}
• Window label: last-keystroke in the window
• Features (F=10):
• duration
• DD_time
• DU_time
• UD_time
• UU_time
• run_avg_duration
• run_avg_DD
• run_avg_DU
• run_avg_UD
• run_avg_UU

Preprocessing

• Windows built with NumPy sliding_window_view.
• Standardization via StandardScaler fitted on training windows only, across all timesteps and samples.

Model

• torch.nn.LSTM (unidirectional, batch_first)
• Hidden size: 64
• Num layers: 1
• Dropout: 0.0
• Head: Linear(hidden_size → 2)

Optimization

• Optimizer: Adam
• LR: 0.001
• Batch size: 256
• Epochs: 30
• Seed: 42

Files

• model.safetensors: model weights
• config.json: architecture + feature metadata
• scaler.joblib: fitted StandardScaler
• metrics.json: classification report + confusion matrix
• inference.py: minimal loading + prediction example

Usage (minimal)

from inference import load_model_and_scaler, predict_df

model, scaler, cfg = load_model_and_scaler("NourFakih/LSTM-32win-Keystrokes")
y_pred = predict_df(df, model, scaler, cfg)  # df must contain cfg["feature_cols"]