Web网络虚拟资源中特定数据检测优化仿真

发布时间：2018-06-28 07:38

本文选题：虚拟资源 + 特定数据　；参考：《计算机仿真》2017年01期

【摘要】：对Web网络虚拟资源中特定数据进行检测,在提高网络虚拟资源安全方面具有重要作用。由于海量Web网络虚拟资源数据量过大,使得资源数据特征多且相似。传统的检测方法主要通过对资源数据特征进行分类筛选来实现数据检测的,当出现大量相似的数据特征时,不能准确地进行分类筛选,导致检测不准确、效率低的问题。提出新的海量Web网络虚拟资源中特定数据检测方法。对海量Web网络虚拟资源背景环境进行分析,采集网络虚拟资源数据集;利用小波变换分析法对采集的数据集进行去噪并归一化处理。引入区域特定数据离群系数算法,获取特定数据与数据集远离程度度量因数,并通过区域离群系数相关距离判断是否存在特定数据,实现对海量Web网络虚拟资源中特定数据进行快速检测。仿真结果表明,改进的检测方法相比传统的检测方法检测效率高,误差低,时间短,具有一定的实用性。
[Abstract]:The detection of specific data in Web virtual resources plays an important role in improving the security of network virtual resources. Because of the large amount of virtual resource data in the massive Web network, the characteristics of the resource data are many and similar. The traditional detection method mainly realizes the data detection by classifying and filtering the resource data features. When a large number of similar data features appear, it can not be accurately classified and screened, which leads to the problem of inaccurate detection and low efficiency. This paper presents a new method for detecting specific data in massive Web network virtual resources. The background environment of massive Web virtual resources is analyzed and the data sets of virtual resources are collected. Wavelet transform method is used to Denoise and normalize the collected data sets. The region specific data outlier coefficient algorithm is introduced to obtain the measure factor of the distance between the specific data and the data set, and to judge the existence of the specific data by the distance correlation between the region outlier coefficient and the data set. To realize the rapid detection of the specific data in the massive Web network virtual resources. The simulation results show that the improved detection method has higher detection efficiency, lower error and shorter time than the traditional detection method.
【作者单位】：中州大学信息工程学院;郑州大学软件与应用科技学院;
【基金】：河南省高等学校重点科研项目(15B520044) 2015年郑州市科技计划项目(153PKJGG143)
【分类号】：TP393.06

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