Deep Learning Intrusion Detection Model Based on Optimized Imbalanced Network Data

To solve the problem of the low detection rate of minority samples in imbalanced datasets in network intrusion detection, a deep learning intrusion detection model based on optimized imbalanced data is proposed. Firstly, a hybrid sampling method is adopted in data processing. Synthetic Minority Over-sampling Technique (SMOTE) was used to increase the numbers of samples in minority categories and the majority of the samples were under-sampled by Neighborhood Cleaning Rule (NCL). Secondly, on the preprocessed balanced dataset, the high-dimensional data was reduced by Deep Belief Network (DBN) to obtain the lower low-dimensional representation of the preprocessed data. Finally, the classification work was completed by Probabilistic Neural Network (PNN). The experiment on NSL-KDD dataset showed that hybrid sampling can improve the detection rate and classification accuracy of minority categories. And the performance of DBN-PNN is obviously superior to the traditional method.