You can deploy this model and start earning money today!

HID-APP/
LSTM-10win-Keystrokes

Name: LSTM-10win-Keystrokes
Rating: 2.5 (8 reviews)

Views

No views yet

LSTM-10win-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: 10
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)

python

from inference import load_model_and_scaler, predict_df

model, scaler, cfg = load_model_and_scaler("NourFakih/LSTM-10win-Keystrokes")
y_pred = predict_df(df, model, scaler, cfg)  # df must contain cfg["feature_cols"]

HID-APP/LSTM-10win-Keystrokes