摘要
为了提高无线动态压缩感知网络的入侵检测能力,提出一种基于多层交叉熵的网络入侵数据自主防御系统设计方法,构建网络入侵数据检测方法,采用大数据挖掘技术进行无线动态压缩感知网络的入侵大数据挖掘,对挖掘的入侵数据采用频谱超分辨识别方法进行特征提取,构建无线动态压缩感知网络入侵检测的动态多层数据分布结构模型,采用关联映射方法进行网络入侵数据的信号结构重组,结合模糊自适应调度方法进行入侵数据的多层交叉熵调度,根据入侵数据的异常性特征分布实现自主检测和入侵特征定位。采用嵌入式的Linux开发工具进行网络入侵数据自主防御系统设计,结合程序加载和交叉编译实现入侵检测算法的自动读写和检测输出。测试结果表明,采用该方法进行网络入侵数据自主防御系统设计,提高了对入侵数据的检测主动性和准确性,从而提高了网络安全性。
In order to improve the intrusion detection ability of wireless dynamic compression aware network,a design method of network intrusion data autonomous defense system based on multi-layer cross-entropy is proposed,and the network intrusion data detection method is constructed.Big data mining technology is used to mine the intrusion of the wireless dynamic compression perceptual network,and the feature extraction of the extracted intrusion data is carried out by using the spectrum super-resolution recognition method.The dynamic multi-layer data distribution structure model of wireless dynamic compression aware network intrusion detection is constructed,and the signal structure reorganization of network intrusion data is carried out by using association mapping method.Combined with fuzzy adaptive scheduling method,multi-layer cross-entropy scheduling of intrusion data is carried out.According to the abnormal feature distribution of intrusion data,autonomous detection and intrusion feature location are realized.The embedded Linux development tool is used to design the network intrusion data self-defense system,and the automatic reading,writing and detecting output of the intrusion detection algorithm are realized by combining program loading and cross-compiling.The test results show that this method can improve the initiative and accuracy of intrusion data detection and improve the network security.
作者
史子新
SHI Zixin(Wuivei Occupational College,Wuwei Gansu 733000,China)
出处
《自动化与仪器仪表》
2020年第1期97-100,共4页
Automation & Instrumentation
基金
2017年甘肃省教育厅科研项目“互联网+全域旅游视角下武威智慧旅游路径探索”(No.2017A-279)
关键词
多层交叉熵
无线网络
入侵检测
自主防御
系统设计
大数据
multilayer cross entropy
wireless network
intrusion detection
autonomous defense
system design
big data
