摘要
为了提高大规模网络自适应安全阈值数据的检测能力,需要对数据进行优化分割,提出基于序贯模式检测和关联规则特征提取的大规模网络自适应安全阈值数据自动分割算法。构建大规模网络自适应安全阈值数据的统计序列模型,提取大规模网络自适应安全阈值数据相似度的描述性统计特征量,结合量化回归分析方法,对提取的大规模网络自适应安全阈值数据的关联特征集进行分类融合,实现大规模网络自适应安全阈值数据的自动分割与建模。仿真结果表明,采用该方法进行大规模网络自适应安全阈值数据自动分割的分类性较好,数据的误分率较低,提高了数据的安全检测和识别能力。
In order to improve the detection ability of adaptive security threshold data in large-scale networks,it is necessary to optimize the segmentation of data.An automatic segmentation algorithm of adaptive security threshold data for large-scale networks based on sequential pattern detection and association rule feature extraction is proposed.The statistical sequence model of large-scale network adaptive security threshold data is constructed,and the descriptive statistical features of similarity of large-scale network adaptive security threshold data are extracted,combined with quantitative regression analysis method.The association feature sets of the extracted large-scale network adaptive security threshold data are classified and integrated,and the automatic segmentation and modeling of the large-scale network adaptive security threshold data are realized.The simulation results show that the method has good classification and low error rate,which improves the security detection and recognition ability of large-scale network adaptive security threshold data.
作者
帅爱华
陈烨
SHUAI Aihua;CHEN Ye(Enshi Polytechnic,Enshi Hubei 445000,China)
出处
《自动化与仪器仪表》
2019年第11期33-36,共4页
Automation & Instrumentation
基金
恩施州打造特色产业发展增长极的路径探索-以恩施州生态富硒产业发展为例(No.HBSXK2017145)
关键词
大规模网络
自适应
安全阈值数据
自动分割
large-scale network
adaptation
security threshold data
automatic segmentation
