摘要
在大数据背景下进行网络异常数据挖掘,提高网络的安全性,提出基于post关键字编译法的网络异常数据挖掘方法,并在嵌入式模块中进行软件开发设计。建立网络异常数据的非线性结构重组模型,采用交叉编译方法进行网络异常数据的关键字特征提取,建立网络异常数据关键字的语义本体模型,采用语义关联映射方法进行网络异常数据的语义特征检测和信息挖掘,构建反映网络异常数据存档信息归类的语义本体模型,通过自相关特征匹配实现网络异常数据的属性检测,结合post关键字编译法方法进行网络异常数据挖掘优化。在嵌入式Linux环境下实现网络异常数据挖掘的软件设计。仿真结果表明,采用该方法进行网络异常数据挖掘的准确性较高,实时性较好,提高了网络异常检测能力。
Under the background of big data,the network abnormal data mining is carried out to improve the security of the network.A network abnormal data mining method based on post keyword compilation method is proposed,and the software development and design are carried out in the embedded module.The nonlinear structure reorganization model of network abnormal data is established,the keyword feature extraction of network abnormal data is carried out by cross-compilation method,the semantic ontology model of network abnormal data keyword is established,the semantic feature detection and information mining of network abnormal data are carried out by using semantic association mapping method,and the semantic ontology model reflecting the classification of archived information of network abnormal data is constructed.The attribute detection of network abnormal data is realized by autocorrelation feature matching,and the optimization of network abnormal data mining is carried out by combining post keyword compilation method.The software design of network abnormal data mining is realized in embedded Linux environment.The simulation results show that the method has high accuracy and real-time performance,and improves the ability of network anoanomaly detection.
作者
焦锐丽
郑武强
李贤
JIAO Rui-li;ZHENG Wu-qiang;LI Xian(Henan Vocational College Of Water Conservancy and Environment,Henan Zhengzhou 450008,China;Zhengzhou Information Engineering Vocational College,Henan Zhengzhou 450008,China)
出处
《新一代信息技术》
2019年第21期45-50,共6页
New Generation of Information Technology
基金
河南水利与环境职业学院(项目编号:KY1828)。
