A Network Intrusion Detection Model Based on K-means Algorithm and Information Entropy

被引:2
作者
Meng, Gao [1 ]
Dan, Li [1 ]
Ni-Hong, Wang [1 ]
Li-Chen, Liu [2 ]
机构
[1] Northeast Forestry Univ, Informat & Comp Engn Coll, Harbin, Heilongjiang, Peoples R China
[2] Northeast Forestry Univ, Coll Mech & Elect Engn, Harbin, Heilongjiang, Peoples R China
来源
INTERNATIONAL JOURNAL OF SECURITY AND ITS APPLICATIONS | 2014年 / 8卷 / 06期
基金
中央高校基本科研业务费专项资金资助;
关键词
Information entropy; K-means algorithm; Dynamic cluster center; Intrusion detection model;
D O I
10.14257/ijsia.2014.8.6.25
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Many factors could influence the clustering performance of K-means algorithm, selection of initial cluster centers was an important one, traditional method had a certain degree of randomness in dealing with this problem, for this purpose, information entropy was introduced into the process of cluster centers selection, and a fusion algorithm combining with information entropy and K-means algorithm was proposed, in which, information entropy value was used to measure the similarity degree among records, the least similar record would be regarded as a cluster center. In addition, a network intrusion detection model was built, it could make cluster centers change dynamically along with the network changes, and the model could real-time update the cluster centers according to actual needs. Experiment results show that the improved algorithm proposed is better than the traditional K-means algorithm in detection ratio and false alarm ratio, and the network intrusion detection model is proved to be feasible.
引用
收藏
页码:285 / 294
页数:10
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