xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses

This repo includes the source code for the "Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses" project. The code is hardware-independent and can be optimized for execution on Google Colab.

Implementation Notes

Testing and running the code is straightforward, giving users the flexibility to utilize either Google Colab or a local machine for their testing purposes.

1. Google Colab:

   git clone https://github.com/CactiLab/code-xNIDS.git

   • Rename the folder

   mv code-xNIDS xNIDS

   • Upload the folder to Colab Notebooks folder under google drive
   • Run the Demo explanation.ipynb notebook
   • Although a GPU is not required, it can significantly accelerate the execution

2. Local Machine:

   pip install -r requirements.txt

   python explanation.py

3. Retrain the DL-NIDS

   • Please carefully update the testcases accordingly in the explantion.py if the users want to retrain the models.
   • kitsune.ipynb contains the reimplementation of kitnet. Please download the dataset and put them under the Data folder.
   • kdd.ipynb includes one Autoencoder and one stateless RNN based DL-NIDS
   • kdd_histroy.ipynb contains one stateful LSTM based DL-NIDS.

Contribute

Contributions are always welcome!

Citation & Paper

The results of this project was published in the paper entitled "xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses" in the USENIX Security 2023. If you want to cite our paper in your work, please use the following BibTeX entry.

@inproceedings{wei2023xnids,
 title = {{xNIDS: Explaining Deep Learning-based Network Intrusion Detection Systems for Active Intrusion Responses}},
 author = {Wei, Feng and Li, Hongda and Zhao, Ziming and Hu, Hongxin},
 booktitle = {{USENIX Security}},
 year = {2023},
}

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