A flow-based network intrusion detection model that integrates class imbalance processing with deep learning: SGM-CNN.

Please cite our papers, in case you find our work useful.

(1) Hongpo Zhang, Lulu Huang, Chase Q. Wu and Zhanbo Li: An Effective Convolutional Neural Network Based on SMOTE and Gaussian Mixture Model for Intrusion Detection in Imbalanced Dataset. Computer Networks (2020), doi:10.1016/j.comnet.2020.107315

(2) Hongpo Zhang, Chase Q. Wu, Shan Gao, Zongmin Wang, Yuxiao Xu and Yongpeng Liu: An effective deep learning based scheme for network intrusion detection, in: 2018 24th International Conference on Pattern Recognition (ICPR), 2018, pp. 682–687. doi:10.1109/ICPR.2018.8546162.

In the following, we also provide a brief user manual for this NIDS.

anaconda3-5.2.0; tensorflow; python3.6; Kreas 2.2.4

The UNSW-NB15 dataset used to support the findings of this study is available at https://www.unsw.adfa.edu.au/unswcanberra-cyber/cybersecurity/ADFA-NB15-Datasets/. The CICIDS2017 dataset used to support the findings of this study is available at https://www.unb.ca/cic/datasets/ids-2017.html.

1. Data preprocessing and feature selection. Different datasets have slightly different data preprocessing processes.
2. Imbalance processing. Contains five types of imbalanced processing technologies, namely ROS, SMOTE, ADASYN, RUS + SMOTE,K-means + SMOTE and SGM.
3. Classification decision. Contains three classification algorithms, namely RF, MLP and CNN.