An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset

Network Intrusion Detection System (NIDS) is a key security device in modern networks to detect malicious activities. However, the problem of imbalanced class associated with intrusion detection dataset limits the classifier's performance for minority classes. To improve the detection rate of minority classes while ensuring efficiency, we propose a novel class imbalance processing technology for large-scale dataset, referred to as SGM, which combines Synthetic Minority Over-Sampling Technique (SMOTE) and under-sampling for clustering based on Gaussian Mixture Model (GMM). We then design a flow-based intrusion detection model, SGM-CNN, which integrates imbalanced class processing with convolutional neural network, and investigate the impact of different numbers of convolution kernels and different learning rates on model performance. The advantages of the proposed model are verified using the UNSW-NB15 and CICIDS2017 datasets. The experimental results show that i) for binary classification and multiclass classification on the UNSW-NB15 dataset, SGM-CNN achieves a detection rate of 99.74% and 96.54%, respectively; ii) for 15-class classification on the CICIDS2017 dataset, it achieves a detection rate of 99.85%. We compare five imbalanced processing methods and two classification algorithms, and conclude that SGM-CNN provides an effective solution to imbalanced intrusion detection and outperforms the state-of-the-art intrusion detection methods.