摘要
在云计算环境下,对高级持续威胁数据的准确挖掘可以提高云计算网络的安全防御能力.高级持续威胁数据具有极值扰动非线性特征,传统的线性处理方法难以实现对这类数据的准确挖掘.提出一种基于极值扰动非线性特征提取的云计算环境下的高级持续威胁数据挖掘仿真模型,对系统载荷运行情况进行评估,得到云计算下的动态任务调配,分析高级持续威胁数据的极值扰动非线性特性,计算高级持续威胁数据的稳态概率,得到极值扰动非线性特征,对非线性特征进行脉冲响应不变周期标记.实现了高级持续威胁数据极值扰动非线性特征的挖掘,构建数据挖掘模型.仿真实验表明,算法对持续威胁数据的正确检测概率在95%以上,数据挖掘性能优越,在云计算环境下的高级持续威胁数据的检测挖掘等领域应用价值较高,为网络安全系统构建等奠定基础.
In the cloud computing environment,advanced persistent threat data accurate mining can improve security and defense capability of the cloud computing network. Advanced persistent threat data have nonlinear characteristics of disturbance linear extremum,and the traditional processing method is difficult to achieve accuracy for this class of data mining. An advanced persistent threat data mining simulation model is proposed based on extreme perturbation nonlinear feature extraction in cloud computing environment,and the system load operation is evaluated,so that the dynamic task allocation in cloud computing is obtained. Extreme value analysis of advanced persistent threat data characteristics of non linear disturbance is made,perturbed nonlinear characteristics are extracted,and the steady state probabilities of senior continued threat data are calculated,so that pulse response invariant cycle marker on the nonlinear characteristic is obtained,the advanced persistent threat data extreme value perturbation of the nonlinear characteristics is extracted,and the data mining model is constructed. Simulation results show that the algorithm for the continuing threat data has better correct detection probability of 95% above,and that data mining has superior performance. It has good application value in the cloud computing for advanced persistent threat data detection and mining,providing foundation for the network security system construction
出处
《石家庄学院学报》
2014年第6期41-46,共6页
Journal of Shijiazhuang University
关键词
云计算
高级持续威胁数据
数据挖掘
非线性特征
网络安全
cloud computing
advanced persistent threat data
data mining
nonlinear characteristic
network security
作者简介
张志宏（1981-），女，山西吕梁人，讲师。主要从事数据挖掘研究．
