The Improvement of Cluster Analysis in Intrusion Detection System

被引：3

作者：

Su, Lin-Ping ^{[1
]}

Zhang, Jian-An ^{[1
]}

机构：

[1] North China Elect Power Univ, Sch Comp Sci & Technol, Beijing 100876, Peoples R China

来源：

2017 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION (ICICTA 2017) | 2017年

关键词：

Data Mining Intrusion Detection; K-Means Algorithm; Clustering Analysis;

D O I：

10.1109/ICICTA.2017.67

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In view of the shortcomings of the traditional clustering algorithm in intrusion detection system, this paper proposes a method of selecting the initial clustering center based on density, which can overcome the problem of K value in ordinary K-Means. The improved intrusion detection model can achieve good clustering effect. Compared with the traditional KMeans, it is found that the improved algorithm can obtain higher detection rate and lower false alarm rate than the traditional K-Means.

引用

页码：274 / 277

页数：4

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