Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches
This repo contains implementation of ML experiments for NIDS in the presence of sampling Please note that repository is noisy, I am planning to clean it up in the future when I have some idle time or you can ask me to prepare you some specific part you are interested in.
Read the published paper here: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9661375
If you find this repo useful in your research, please consider citing:
@ARTICLE{9661375, author={Alikhanov, Jumabek and Jang, Rhongho and Abuhamad, Mohammed and Mohaisen, David and Nyang, Daehun and Noh, Youngtae}, journal={IEEE Access}, title={Investigating the Effect of Traffic Sampling on Machine Learning-Based Network Intrusion Detection Approaches}, year={2022}, volume={10}, number={}, pages={5801-5823}, doi={10.1109/ACCESS.2021.3137318}}
Related work on feature sestimation error of sFlow and SketchFlow samplers comparisons are experimented here: https://github.com/Jumabek/nids-with-sampling/blob/master/FlowFeatureEstimationOfTrafficSamplers4NIDS.pptx.pdf