摘要
为了解决大规模、高维度无线网络安全状态监测过程中存在的数据相似性以及差异性问题,研究基于大数据分析技术的无线网络安全状态监测新方法。采用基于语义Agent的无线网络大数据采集方式获取网络数据,使用基于图论的模糊聚类方法,根据模糊界划分网络数据的模糊等价关系,对无线网络安全因素进行聚类,实现无线网络安全因素的关联分析。利用CVSS大数据分析技术对每个安全因素的利用率进行权重赋值,并建立节点间的依赖联系,再依据危险源信息的传播途径,量化判断无线网络安全受到威胁的概率,完成无线网络安全状态监测。实验结果表明,该方法在数据量达到3500 bit时,仅需不到6 s的时间即可展现出高效的分类性能。
In order to solve the problem of data similarity and difference existing in the process of largescale and high-dimensional wireless network security status monitoring,a new method of wireless network security status monitoring based on big data analysis technology is studied.The semantic agentbased wireless network big data acquisition method is adopted to obtain network data.Based on the fuzzy equivalence relation of network data divided by fuzzy boundary,the security factors of wireless network are cluster,and the correlation analysis of security factors of wireless network is realized.CVSS big data analysis technology is used to assign weight to the utilization rate of each security factor,and establish the dependency relationship between nodes.Then,according to the transmission path of danger source information,the probability of wireless network security being threatened is quantitatively judged,and the status monitoring of wireless network security is completed.The experimental results show that the method only takes less than 6 s time when the data volume reaches 3500 bit,and shows efficient classification performance.
作者
张亚楠
ZHANG Yanan(The Geological Museum of China,Beijing 100034,China)
出处
《电子设计工程》
2025年第7期164-167,共4页
Electronic Design Engineering
关键词
大数据分析技术
无线网络
安全状态监测
模糊聚类
关联分析
权重赋值
big data analysis technology
wireless network
safety condition monitoring
fuzzy clustering
correlation analysis
weight assignment
