A Kind of Network Intrusion Detection Method Using Improved Support Vector Machine Based on Ant Colony Algorithm

被引：0
|
作者：
Zhang, Xiaoqin [1 ]
Jia, Guojun [1 ]
机构：
[1] Shanxi Normal Univ, Sch Math & Comp Engn, Linfen 041004, Shanxi, Peoples R China
来源：
INFORMATION TECHNOLOGY APPLICATIONS IN INDUSTRY, PTS 1-4 | 2013年 / 263-266卷
关键词：
support vector machine (SVM); feature extraction; ant colony algorithm; Intrusion detection;
D O I：
10.4028/www.scientific.net/AMM.263-266.2995
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Support vector machine (SVM) is suitable for the classification problem which is of small sample, nonlinear, high dimension. SVM in data preprocessing phase, often use genetic algorithm for feature extraction, although it can improve the accuracy of classification. But in feature extraction stage the weak directivity of genetic algorithm impact the time and accuracy of the classification. The ant colony algorithm is used in genetic algorithm selection stage, which is better for the data pretreatment, so as to improve the classification speed and accuracy. The experiment in the KDD99 data set shows that this method is feasible.
引用
收藏
页码：2995 / 2998
页数：4
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