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First_Trained_Model.pth

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README.md

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+# LSTM IDS Models for CICIDS2017 Dataset
+
+This repository provides two trained PyTorch LSTM models for network intrusion detection, trained on the CICIDS2017 dataset. These models are designed for use in research, benchmarking, or as a starting point for further development in network security and anomaly detection tasks.
+
+## 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
+   import torch
+   from your_model_definition import IdsRnn  # Use the same architecture as in training
+
+   model = IdsRnn(hidden_size=512, output_size=2)  # or output_size=7 for multi-class
+   model.load_state_dict(torch.load('lambda_with_valid.pth'))
+   model.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
+MIT License
+
 ---
-license: agpl-3.0
----
+
+If you use these models in your research or project, please cite or reference this repository.

Second_Trained_Model.pth

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