Prediction of Network Security Perception Based on Attention Mechanism and Convolutional Neural Network
In order to improve the effectiveness of network security defense,attention mechanism and convolutional neural network have become the focus of research,but the traditional scheme may bring problems such as overfitting model,high computing and memory overhead,and lack of spatial context modeling.To solve the above problems,a network security perception prediction method based on attention mechanism and convolutional neural network is studied.Through four steps of deepening the network structure,adding dropout layer,data normalization,and data fusion,an improved squeeze and excitation network scheme is finally obtained.The experimental results show that the convergence rate of the scheme is fast,and the final accuracy rate is 97.3％ after 65 iterations.In the case of fusion of five data,the accuracy rate is up to 97.5％,indicating that the network security perception prediction model established in this study has high accuracy and strong generalization ability.
万方数据
维普
