基于优化蚁群算法的网络流量数据精准挖掘仿真  

Simulation of Accurate Mining of Network Traffic Data Based on Optimized Ant Colony Algorithm

作　　者：潘立琼[1] 张健[1] PAN Li-qiong;ZHANG Jian(Anhui Sanlian University,Hefei 230601,China)

机构地区：[1]安徽三联学院

出　　处：《佳木斯大学学报（自然科学版）》2020年第1期53-57,共5页Journal of Jiamusi University：Natural Science Edition

基　　金：2019年度安徽省教育厅高校自然科学重点项目(KJ2019A0903);2018年度安徽省教育厅自然科学重点项目(KJ2018A0607);2018年度安徽省质量工程项目(2018mooc088)

摘　　要：为了更好的提高网络流量数据挖掘准确性,提出基于优化蚁群算法的网络流量数据精准挖掘仿真方法。结合蚁群算法对网络数据异常特征数值进行采集,并根据采集结果进行归类计算,根据数据特征归类标准对网络流量挖掘步骤进行优化,最终实现对网络流量数据的精准挖掘。最后通过实验证实,传统方法数值波动范围在±15之间,而所提方法数值波动范围在±5之间,检测精准度更高,具有较高的实际应用性。In order to improve the accuracy of network traffic data mining, a simulation method of network traffic data precision mining based on optimal ant colony algorithm is proposed. Combined with ant colony algorithm, the abnormal feature value of network data is collected, and the network traffic mining steps are optimized according to the data feature classification standard, and the accurate mining of network traffic data is finally realized. Finally, the experimental results show that the numerical fluctuation range of the traditional method is between ±15, while the numerical fluctuation range of the proposed method is between ±5, which has higher detection accuracy and higher practical application.

关 键 词：优化蚁群 网络流量 数据挖掘 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]