基于模糊神经网络的异常网络数据挖掘算法

doi:10.11896/j.issn.1002-137X.2019.04.011

摘要/Abstract

摘要： 异常网络数据受到聚类中心的模糊加权扰动的影响,导致数据挖掘的聚类性不好。文中提出一种基于模糊神经网络的异常网络数据挖掘算法,该算法根据异常网络数据的混合分类属性进行相似度分析,提取异常网络数据的数值属性特征和分类属性特征,采用联合关联规则分析方法进行异常网络数据的模糊融合处理,采用基于模糊质心相异性的度量方法构建异常网络数据的分类模糊集,并在模糊数据集中进行异常网络数据混合加权和自适应分块匹配,进而提取异常网络数据的弱关联化特征量,最后将提取的特征量输入到模糊神经网络分类器中进行数据分类识别,完成异常网络数据的优化挖掘。仿真结果表明,采用所提方法进行异常网络数据挖掘的数据聚类性较好,挖掘过程的收敛性和抗干扰性较强。

关键词: 模糊神经网络, 特征提取, 挖掘, 异常网络数据

Abstract: Abnormal network data are affected by the fuzzy weighted disturbance of the clustering center,which leads to poor clustering of data mining.This paper proposed a data mining algorithm of anomaly network based on fuzzy neural network.Similarity analysis is performed according to the mixed classification attributes of abnormal network data,the numerical and classification attributes of abnormal network data are extracted,and the fuzzy fusion processing of the abnormal network data is carried out by using the joint association rule analysis method.The classification fuzzy set of abnormal network data is constructed based on fuzzy centroid heterogeneity measurement method.In the fuzzy data set,abnormal network data are weighted by mixing and adaptive block matching,and the weak correlation characteristic quantity of abnormal network data is extracted.The extracted features are input into the fuzzy neural network classifier for data classification and recognition,and the optimized mining of abnormal network data is completed.The simulation results show that the proposed method has good data clustering ability for anomaly network data mining.The mining process has strong convergence and anti-interference.

Key words: Abnormal network data, Feature extraction, Fuzzy neural network, Mining

中图分类号:

TP391

许磊, 王建新. 基于模糊神经网络的异常网络数据挖掘算法[J]. 计算机科学, 2019, 46(4): 73-76. https://doi.org/10.11896/j.issn.1002-137X.2019.04.011

XU Lei, WANG Jian-xin. Data Mining Algorithm of Abnormal Network Based on Fuzzy Neural Network[J]. Computer Science, 2019, 46(4): 73-76. https://doi.org/10.11896/j.issn.1002-137X.2019.04.011

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