摘要
本文提出了基于信息熵的大规模网络流量异常分类方法。该方法综合运用子空间方法和k-means分类方法,并以校园网为实验环境实现了网络流量异常分类实验。实验结果表明,基于信息熵的大规模网络流量异常分类实现简单、计算量小,分类准确性高。
This paper presents an entropy-based large-scale network traffic anomaly classification method for the integrated use of the subspace method and the k-means clustering method.And classifying network traffic anomalies is realized in the experimental environment of campus networks.The experimental results show that large-scale traffic anomaly classification based on entropy not only realizes simple and has a small computation quantity,but also has a high classification precision.
出处
《计算机工程与科学》
CSCD
2007年第2期40-43,共4页
Computer Engineering & Science
关键词
信息熵
子空间方法
大规模网络流量
异常分类
entropy,subspace method,large-scale network traffic,anomaly classification
