基于改进小波神经网络的大数据网络安全态势预测  

Security Situation Prediction of Big Data Networks Based on Improved Wavelet Neural Networks

作　　者：高义梅[1] 韦号 郭俊萍 张晓美[1] 李筱竹[1] GAO Yimei;WEI Hao;GUO Junping;ZHANG Xiaomei;LI Xiaozhu(China Meteorological Administration Public Meteorological Service Center,Beijing 100081,China)

机构地区：[1]中国气象局公共气象服务中心,北京100081

出　　处：《信息与电脑》2023年第22期211-213,共3页Information & Computer

摘　　要：为提升网络安全态势预测的精度,提出使用动量因子(Momentum Factor,MF)改进小波神经网络(Wavelet Neural Network,WNN),形成MF-WNN的网络安全态势要素提取模型,通过MF对WNN的权值进行精度优化。实验结果表明,与其他3种常用的预测模型相比,基于改进WNN的预测模型具有较高的精确度。In order to improve the prediction accuracy of network security situation,the article proposes to use Momentum Factor(MF)to improve the Wavelet Neural Network(WNN),forming an MF-WNN network security situation element extraction model.The weight of the wavelet neural network is optimized for accuracy through MF.The experimental results show that the improved wavelet neural network prediction model proposed in the article has higher accuracy compared to the other three commonly used prediction models.

关 键 词：网络安全 态势要素 提取模型 小波神经网络(WNN) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]