A Deep Learning Based Semi-Supervised Network Intrusion Detection System Robust to Adversarial Attacks.

• Paper Link
• Unofficial PDF Version

1. Semi-Supervised Learning Model
2. Adversarial Testing

• Python 3.8
• pandas 1.5.3
• numpy 1.22.4
• pytorch 2.0.1 cu118
• notebook 6.4.8

Dataset is in Dataset_NSLKDD_2 directory. Follow the notebook Helper Notebooks/NIDS_Trainer.ipynb for training and testing.

Follow the notebooks Helper Notebooks/NIDS_Testing_IDSGAN.ipynb and Helper Notebooks/BlackBoxIDS.ipynb for testing.

If you use this code for publication, please cite the original paper.

@inproceedings{nids-nsyss-2023,
  title={A Deep Learning Based Semi-Supervised Network Intrusion Detection System Robust to Adversarial Attacks},
  author={Mukit Rashid, Syed Md. and Toufikuzzaman, Md. and Hossain, Md. Shohrab},
  booktitle={10th International Conference on Networking Systems and Security (NSysS)},
  year={2023},
  address = {Dhaka, Bangladesh},
  volume={},
  number={},
  pages={25--34},
  doi= {https://doi.org/10.1145/3629188.3629189},
  publisher = {ACM}
}

For help or issues using this codebase, please submit a GitHub issue.

For personal communication related to this codebase, please contact Md. Toufikuzzaman ([email protected]).