Network Intrusion Detection Technology Based on Convolutional Neural Network
With the development of the Internet,cybersecurity is a serious challenge for the development of the Inter-net,and network intrusion detection technology has become one of the issues that need to be focused on.Especially with the further diversification of attack methods and the continuous increase of data dimension,the traditional ma-chine learning algorithms can no longer meet the requirements of current network intrusion detection system.Convolu-tional neural networks(CNN)have powerful feature extraction capabilities and data analysis capabilities,which can improve the accuracy and timeliness of network intrusion detection.Therefore,CNN is applied to network intrusion de-tection technology,and the detection accuracy is improved through the cross entropy loss function.Firstly,the public dataset is preprocessed.Then,the CNN model is constructed to obtain the classification prediction results.Finally,the model evaluation index is calculated,and the CNN model is continuously adjusted until the expected value of the model evaluation index is reached.
NETL
NSTL
万方数据
