分布式环境下用户网络信息安全性检测仿真

发布时间：2018-05-03 10:57

本文选题：分布式环境 + 用户网络信息　；参考：《计算机仿真》2017年08期

【摘要】：分布式网络环境下对用户网络信息安全性检测,能够真正解决网络安全问题。对网络的安全性进行检测,需要根据网络本体特征得到向量机类中心最远的距离,定义隶属度函数,完成对网络信息安全性检测。传统方法结合支持向量机理论对信息属性值进行划分聚类,最后将划分聚类结果输出,但忽略了建立隶属度函数,导致检测结果不准确。为解决上述问题,提出基于模糊支持向量机(FSVM)理论的分布式环境下用户网络信息安全检测方法。对用户网络信息梯度最大值进行自适应加权处理,得到输出用户网络信息关联指向性加权向量,结合模糊支持向量机理论实现用户需求本体特征指向性波束聚类,以模糊支持向量机类中心最远的距离作为度量定义隶属度函数,并以此为依据完成分布式环境下用户网络信息安全检测。实验结果表明,所提方法能够有效提升分布式环境下用户网络信息安全检测效率,且检测效果较好。
[Abstract]:In the distributed network environment, the security detection of user network information can solve the problem of network security. In order to detect the network security, we need to get the furthest distance of the vector machine class center according to the network ontology feature, define the membership function, and complete the security detection of the network information. The traditional method combines the support vector machine theory to classify the information attribute value, and finally outputs the partition clustering result, but neglects the establishment of the membership function, which leads to the inaccurate detection results. In order to solve the above problems, a security detection method of user network in distributed environment based on fuzzy support vector machine (FSVM) theory is proposed. The maximum information gradient of user network is processed by adaptive weighting, and the weighted vector of output user network information is obtained. Combined with fuzzy support vector machine theory, user demand ontology feature directivity beam clustering is realized. The farthest distance from the center of fuzzy support vector machine (FSVM) is taken as the metric to define membership function, and based on this, the information security detection of user network in distributed environment is completed. The experimental results show that the proposed method can effectively improve the detection efficiency of user network information security in distributed environment, and the detection effect is good.
【作者单位】：武夷学院实验中心;
【基金】：福建省教育厅科技项目(JK2012056) 福建省科技厅科技项目(JK2016041)
【分类号】：TP393.08

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