In order to improve the accuracy of internet intrusion detection methods,an intrusion detection method combining convolution neural network and attention mechanism is proposed.Using Borderline-SMOTE oversampling algorithm and MinMax normalization to preprocess data,effectively alleviate the problem of large differences in the amount of intrusion data,and improve the detection performance of unbalanced data;the convolution neural network inception structure is used for multi-scale feature extraction of data,and the attention mechanism is used for dimension update to improve the accuracy of feature expression when the model processes massive data.The experiment shows that the average accuracy of the intrusion detection method is 99.57%.Compared with SVM,CNN,RNN,and BLS-GMM,the accuracy increases by 4.48%,1.35%,1.62%and 0.04%respectively,and the recall increases by 4.48%,1.36%,1.62%and 0.14%respectively.
关键词
入侵检测/卷积神经网络/注意力机制/过采样算法/非平衡数据
Key words
intrusion detection/deep learning/attention mechanism/oversampling algorithm/unbalanced data
维普
万方数据