动态特征融合的域自适应入侵检测方法研究OA

Aiming at the problems of incomplete feature extraction and limited model generalization ability in intrusion detection research,a domain adaptive intrusion detection method with dynamic feature fusion is proposed.Firstly,a convolutional neural network is used to extract spatial features,while a bidirectional long short-term memory network is utilized for temporal feature extraction.This approach enables comprehensive extraction of multi-dimensional feature information from network traffic data.Secondly,the uncertainty is measured by calculating the information entropy of the two features,and different weights are assigned according to the entropy value,and the extracted features are weighted and fused according to the weights.Finally,during the training process,the proposed adaptive domain weight loss algorithm is used to dynamically adjust the contribution of the source domain and target domain data to improve the generalization ability of the model on the target domain data.Experiments are carried out using the NSL-KDD and UNSW-NB15 datasets.Compared with the existing mainstream methods,this method has higher detection accuracy,which is 0.856 3 and 0.916 respectively.