大数据技术在网络安全分析中的应用研究

针对误用检测方法和异常检测方法缺少对网络风险因素分析,导致网络漏洞检出率较低的问题,提出了基于大数据Hadoop技术检测网络安全漏洞研究。构建标准化直接关系矩阵、生成总关系矩阵确定网络风险元素属性,由此分析网络风险。构建Hadoop框架,采集入侵行为漏洞信息,搭建核心装置,处理网络漏洞。避免外力干扰情况下,评估网络漏洞检测率水平,设计漏洞检测流程。搭建实验环境,设定攻击模式,由实验结果可知,基于大数据Hadoop技术漏洞检出率无论是静态网络环境还是动态网络环境,检出率最高都为94%,具有良好检测效果,为网络安全运行提供技术支持。

Application of big data technology in network security analysis

In view of the lack of analysis of network risk factors for misuse detection methods and anomaly detection methods,resulting in a low detection rate of network vulnerabilities,in the face of this problem,a research on detecting network security vulnerabilities based on big data Hadoop technology is proposed.A standardized direct relationship matrix is constructed a total relationship matrix is generated to determine the attributes of network risk elements,thereby network risks are analyzed.Build a Hadoop framework,collect intrusion behavior vulnerability information,build a core device,and handle network vulnerabilities.Under the condition of avoiding external interference,assess the level of network vulnerability detection rate and design the vulnerability detection process.Set up an experimental environment and set an attack mode.From the experimental results,we can see that the detection rate of vulnerabilities based on big data Hadoop technology,whether it is a static network environment or a dynamic network environment,the highest detection rate is 94%,which has a good detection effect and is network security can provide technical support for operation.