Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features,a detection method based on VAE-CWGAN and fusion of statistical im-portance of features was proposed.Firstly,data preprocessing was conducted to enhance data quality.Secondly,a VAE-CWGAN model was constructed to generate new samples,addressing the problem of imbalanced datasets,ensuring that the classification model no longer biased towards the majority class.Next,standard deviation,difference of median and mean were used to rank the features and fusion their statistical importance for feature selection,aiming to obtain more representative features,which made the model can better learn data information.Finally,the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets,namely NSL-KDD,UNSW-NB15,and CIC-IDS-2017.The accuracy rates are 98.95%,96.24%,and 99.92%,respectively,effectively improving the perfor-mance of intrusion detection.
关键词
入侵检测/网络流量/类不平衡/特征选择/统计重要性融合
Key words
intrusion detection/network traffic/class imbalance/feature selection/fusion of statistical importance
维普
万方数据