LSTM IDS Models for CICIDS2017 Dataset

Models Included

• lambda_with_valid.pth: LSTM model trained for binary classification (benign vs. attack) using cross-validation.
• mapping_with_valid.pth: LSTM model trained for multi-class attack categorization using cross-validation.

Model Details

• Architecture: LSTM-based Recurrent Neural Network
• Input Features: 80 per sample (preprocessed from CICIDS2017)
• Training: 5-fold cross-validation, early stopping, Adam optimizer
• Framework: PyTorch

Usage

1. Download the .pth files from this repository.
2. Load the model in your PyTorch code:
   python

   1import torch 2from your_model_definition import IdsRnn # Use the same architecture as in training 3 4model = IdsRnn(hidden_size=512, output_size=2) # or output_size=7 for multi-class 5model.load_state_dict(torch.load('lambda_with_valid.pth')) 6model.eval()
3. Prepare your input data with the same preprocessing as used during training.
4. Run inference as needed.

Notes

• These models require the same feature extraction and preprocessing pipeline as described in the original training code.
• For best results, refer to the full training pipeline and preprocessing steps.

License