SVM-based network intrusion detection model

被引：0

作者：

Zhang, Kun ^{[1
,2
]}

Cao, Hong-Xin ^{[1
]}

Liu, Feng-Yu ^{[1
]}

Li, Qian-Mu ^{[1
]}

机构：

[1] School of Computer Science and Technology, NUST, Nanjing 210094, China

[2] Computer Department, Nanjing University, Nanjing 210093, China

来源：

Nanjing Li Gong Daxue Xuebao/Journal of Nanjing University of Science and Technology | 2007年 / 31卷 / 04期

关键词：

Algorithms - Computer networks - Security of data - Support vector machines;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In view of the problems of using traditional machine learning method to detect the network intrusions, this paper proposes a network intrusion detection model based on support vector machine (SVM). Experimental results demonstrate that the proposed model has higher detection accuracy of intrusions and avoids the limitation of the detection methods based on traditional machine learning. In the training, considering the effect of different network data features on the intrusion detection results, a new weighted feature classification method is also brought forward, which improves the accuracy of network intrusion detection.

引用

页码：403 / 408