Network Intrusion Detection

This repository contains the implementation of a network intrusion detection system using state-of-the-art and hybrid GNNs.

Datasets

The following datasets were used in this project:

1. NSL-KDD: A dataset that improves upon the KDD Cup 1999 dataset, containing various network traffic features and attack types.

   • NSL-KDD Dataset

2. UNSW-NB15: A comprehensive dataset with a wide range of modern attack types and normal network traffic.

   • UNSW-NB15 Dataset

3. CICIDS2017: A dataset that includes various network traffic data with different types of attacks.

   • CICIDS2017 Dataset

4. KDD Cup 1999: A classic dataset used for intrusion detection, containing a large amount of network traffic data.

   • KDD Cup 1999 Dataset

Approach

The project follows a structured approach to build and evaluate GNN-based network intrusion detection models

Requirements

To run the code in this repository, you need the following Python libraries:

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• xgboost
• torch_geometric
• torch

Usage

1. Clone the repository:

   git clone https://github.com/Abrar2652/network-intrusion-detection.git
   cd network-intrusion-detection

2. Download the datasets:

   • Download the datasets from the provided links and place them in the data directory.

3. Run the Jupyter Notebook:

   • Open the Jupyter Notebook network-intrusion-detection.ipynb and run the cells to preprocess the data, train the models, and evaluate the results.

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements or suggestions.

License

This project is licensed under the MIT License.

Acknowledgments

• The authors of the NSL-KDD, UNSW-NB15, CICIDS2017, and KDD Cup 1999 datasets for providing valuable data for research.
• The open-source community for providing tools and libraries that made this project possible.