摘要
针对目前无线融断网络威胁环境自适应检测方法受噪音影响较大,容易出现信噪比过高、响应时间过短的问题,提出一种基于深度学习的无线融断网络威胁环境自适应检测方法。以梯度检测作为先验条件实现自适应去噪,基于深度学习对离散网络信号进行重构,构建函数检测模型,分析信号序列值,通过对比获得异常信号检测模型,从而实现网络威胁环境自适应检测。实验结果表明,基于深度学习的无线融断网络威胁环境自适应检测方法能够有效提高信噪比,缩短响应时间。
In order to solve the problems of high signal⁃to⁃noise ratio and short response time,an adaptive detection method based on deep learning is proposed in this paper.The gradient detection is used as a priori condition to realize adaptive denoising.The discrete network signal is reconstructed based on deep learning.The function detection model is constructed,the signal sequence value is analyzed,and the abnormal signal detection model is obtained by comparison,so as to realize the adaptive detection of network threat environment.The experimental results show that the method based on deep learning can effectively improve the SNR and shorten the response time.
作者
陈金生
CHEN Jinsheng(Shandong Zhengyuan Yeda Technology Co.,Ltd.,Weifang 261021,China)
出处
《电子设计工程》
2021年第16期83-86,91,共5页
Electronic Design Engineering
基金
山东省自然科学基金(61973007)。
关键词
深度学习
无线融断网络
威胁环境
自适应检测方法
deep learning
wireless fusion network
threat environment
adaptive detection method
