Network intrusion detection model SGM-CNN based on class imbalance processing

For the data class imbalance problem, the present invention provides an effective network intrusion detection model SGM-CNN based on a Synthetic Minority Over-Sampling Technique (SMOTE) and a GaussianMixture Model (GMM) based on a data flow. According to the technical scheme, the method comprises the steps of firstly obtaining a to-be-identified network data flow; and preprocessing the data stream, inputting the preprocessed data stream into a pre-established network intrusion detection model based on a one-dimensional convolutional neural network (1D CNN), and outputting a detection result of the network data stream. The invention provides a class imbalance processing technology, namely an SGM, for large-scale data. The SGM firstly uses SMOTE to perform oversampling on minority class samples, then uses GMM to perform clustering-based downsampling on majority class samples, and finally balances data of each class. According to the SGM method, expensive time and space cost caused by oversampling is avoided, th