计算机应用研究2012，Vol.29Issue(9)：3411-3414,4.DOI:10.3969/j.issn.1001-3695.2012.09.056
基于GA-CFS和AdaBoost算法的网络流量分类
Network traffic classification based on GA-CFS and AdaBoost algorithm
剌婷婷 1师军1
作者信息
- 1. 陕西师范大学计算机科学学院,西安710062
- 折叠
摘要
Abstract
The selection of feature attribute plays an important role in the network traffic classification. This paper applied a method considering the CFS algorithm as the fitness function of the improved genetic algorithm (GA-CFS) in order to extract the main flow statistical attributes in the space of 249 attributes and selected 18 attributes of a flow as the best feature subset. Finally it used the AdaBoost algorithm to enhance a series of weak classifiers to the strong classifiers. At the same time, it fulfilled the classification of the network traffic, and further studied the network traffic intensively. The experimental results indicate that GA-CFS and AdaBoost algorithm can achieve higher classification precision compared with the weak classifiers.关键词
流量分类/相关性特征选择/适应度函数/AdaBoost算法/弱分类器/权重Key words
traffic classification/CFS/fitness function/AdaBoost algorithm/weak classifier/weight分类
信息技术与安全科学引用本文复制引用
剌婷婷,师军..基于GA-CFS和AdaBoost算法的网络流量分类[J].计算机应用研究,2012,29(9):3411-3414,4.
