多尺度卷积与双注意力机制融合的入侵检测方法.

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. [ABSTRACT FROM AUTHOR]