Code for infocom 2021 paper 'MANDA: On Adversarial Example Detection for Network Intrusion Detection System'
- Implement annoconda following the instruction in https://www.anaconda.com/. Anaconda will help to manage the learning environement.
- reate your env using the environment.yml file included in the repository. All the required libraries (including tensorflow and python) will be implemented. The default environment name is tf1_python3 as specified in the yml file. If you would like to use another name, edit the first line of the environment.yml file.
Create your env
conda env create --file environment.yml
Activate your environment.
conda activate tf1_python3
find the scripts in folder scripts
- run 'bash adv_craft.sh' for crafting adversarial examples. There are several configurable params for selecting the specific attack.
- run 'bash adv_detect.sh' for detecting the crafted AEs. There are also several configurable params.
Please cite
@article{wang2022manda, title={Manda: On adversarial example detection for network intrusion detection system}, author={Wang, Ning and Chen, Yimin and Xiao, Yang and Hu, Yang and Lou, Wenjing and Hou, Thomas}, journal={IEEE Transactions on Dependable and Secure Computing}, year={early access, 2022}, }
or
@INPROCEEDINGS{9488874, author={Wang, Ning and Chen, Yimin and Hu, Yang and Lou, Wenjing and Hou, Y. Thomas}, booktitle={IEEE INFOCOM 2021 - IEEE Conference on Computer Communications}, title={MANDA: On Adversarial Example Detection for Network Intrusion Detection System}, year={2021}, volume={}, number={}, pages={1-10}, doi={10.1109/INFOCOM42981.2021.9488874}}