基于统计学特征的网络信息安全漏洞检测建模研究  

Research on Network Information Security Vulnerability Detection Modeling Based on Statistical Features

作　　者：张清松 ZHANG Qing-song(Nanjing Vocationgal Insitute Mechatronic Technologty,Nanjing 211306 China)

机构地区：[1]南京机电职业技术学院,江苏南京211306

出　　处：《自动化技术与应用》2024年第12期143-145,210,共4页Techniques of Automation and Applications

摘　　要：采用传统方法检测网络信息安全漏洞时,检测结果的TPR值和Accuracy值低、FPR值高、ROC曲线检测结果差。基于此,提出基于统计学特征的网络信息安全漏洞检测建模研究方法。先通过交叉融合算法建立网络信息数据获取模型,对网络信息和安全漏洞进行获取,采用多模融合算法建立网络信息安全漏洞时间序列,然后根据模糊信息聚类算法计算,结合交叉融合与特征匹配算法提取网络信息安全漏洞分布特征量,最后综合考虑SVM的线性与非线性情况,构建网络信息安全漏洞检测模型。实验结果表明,通过所提方法的TPR值和Accuracy值高、FPR值低、ROC曲线检测结果好。When using traditional methods to detect network information security vulnerabilities,the TPR value and accuracy value of the detection results are low,the FPR value is high,and the ROC curve detection results are poor.Based on this,a research method of network information security vulnerability detection modeling based on statistical characteristics is proposed.Firstly,the network information data acquisition model is established through the cross fusion algorithm to obtain the network information and security vulnerabilities.The multi-mode fusion algorithm is used to establish the time series of network information security vulnerabilities.Then,the distribution characteristics of network information security vulnerabilities are extracted according to the fuzzy information clustering algorithm and combined with the cross fusion and feature matching algorithm,Finally,considering the linear and nonlinear conditions of SVM,a network information security vulnerability detection model is constructed.The experimental results show that the proposed method has high TPR value and accuracy value,low fpr value and good ROC curve detection results.

关 键 词：统计学特征 网络信息安全 漏洞检测 模型 支持向量机 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]