基于模糊神经网络的异常网络数据挖掘算法
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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。
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.
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.
引文
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[2] ZHANG S,ZHANG L.W-PAM restricted clustering algorithm in data mining[J].Computer Science,2016,43(S2):447-450.(in Chinese)张松,张琳.一种数据挖掘中的W-PAM限制聚类算法[J].计算机科学,2016,43(S2):447-450.
[3] PANG T J,LIANG J Y.A large standard hybrid data clustering algorithm based on sampling[J].Computer Science,2016,43(9):209-212.(in Chinese)庞天杰,梁吉业.一种基于抽样的大规范混合数据聚类集成算法[J].计算机科学,2016,43(9):209-212.
[4] MORADI M,KEYVANPOUR M R.An analytical review of XML association rules mining[J].Artificial Intelligence Review,2015,43(2):277-300.
[5] DONG G L,RYU K S,BASHIR M,et al.Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J].Journal of Medical Systems,2013,37(2):1-10.
[6] KHALILI A,SAMI A.SysDetect:a systematic approach to cri- tical state determination for industrial intrusion detection systems using Apriori algorithm[J].Journal of Process Control,2015,2776:154-160.
[7] MERNIK M,LIU S H,KARABOGA M D,et al.On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation[J].Information Sciences,2015,291(10):115-127.
[8] ZHAO X J,SUN Z X,YUAN Y.An efficient association rule mining algorithm based on predetermined screening[J].Journal of Electronics & Infromation Technology,2015,37(7):1620-1625.
[9] XU K Y,GONG X R,CHENG M C.An audit log association rule mining based on improved Apriori algorithm[J].Journal of Computer Applications,2016,36(7):1847-1851.
[10] LI M D,ZHAO H,WENG X W,et al.Differential evolution algorithm based on optimal gaussian random walk and individual selection strategy[J].Control and Decision,2016,31(8):1379-1386.
[11] BI S,HO C K,ZHANG R.Wireless powered communication:opportunities and challenges[J].IEEE Communications Magazine,2015,53(4):117-125.
[12] SUN L J,CHEN X D,HAN C,et al.New Fuzzy-Clustering Algorithm for Data Stream[J].Journal of Electronics & Infromation Technology,2015,37(7):1620-1625.
[13] BI A Q,DONG A M,WANG S T.A dynamic data stream clus- tering algorithm based on probability and exemplar[J].Journal of Computer Research and Development,2016,53(5):1029-1042.
[14] XING C Z,LIU J.Evolutionary data stream clustering algorithm based on integration of affinity propagation and density[J].Journal of Computer Applications,2015,35(7):1927-1932.
[15] CHEN H,WAN G X,XIAO Z J.Intrusion detection method of deep belief network model based on optimization of data processing[J].Journal of Computer Applications,2017,37(6):1636-1643.
[16] ZHANG X C,SANG R T,ZHOU Z H,et al.A Short Text Classification Method Based on Two-Channel Convolutional Neural Network [J].Journal of Chongqing University of Technology(Natural Science),2019,33(1):45-52.(in Chinese)张小川,桑瑞婷,周泽红,等.一种基于双通道卷积神经网络的短文本分类方法法[J].重庆理工大学学报(自然科学),2019,33(1):45-52.