Implementation of an Anomaly Detection Model Based on Improving Clustering Analysis

LI Han

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Implementation of an Anomaly Detection Model Based on Improving Clustering Analysis

更新时间：2026-09-04

- Implementation of an Anomaly Detection Model Based on Improving Clustering Analysis
- Vol. 27, Issue 8, Pages: 66-69(2010)
- 作者机构：
  
  北京信息科技大学机构理学院,北京,100192
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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李涵. 一种改进的聚类方法在异常检测中的应用[J]. 微电子学与计算机, 2010, 27(8): 66-69.

LI Han. Implementation of an Anomaly Detection Model Based on Improving Clustering Analysis[J]. 2010, 27(8): 66-69.
李涵. 一种改进的聚类方法在异常检测中的应用[J]. 微电子学与计算机, 2010, 27(8): 66-69. DOI：

LI Han. Implementation of an Anomaly Detection Model Based on Improving Clustering Analysis[J]. 2010, 27(8): 66-69. DOI：

摘要

入侵检测系统是一种积极主动的安全防护技术

它是信息安全保护体系结构中的一个重要组成部分.异常检测是入侵检测的一种方法

因其能够检测出未知的攻击而受到广泛的研究.以基于数据挖掘的异常检测技术为研究内容

以提高异常检测的检测率、降低误报率为目标

以聚类分析为主线

提出了一种改进的聚类检测算法和模型

并进行仿真实验.算法首先去除了数据集中明显的噪声和孤立点

通过分裂聚类、合并聚类以及利用超球体的密度半径确定个初始聚类中心

以减小初始值的选取对聚类结果造成的影响

提高异常检测效率

并以此构造入侵检测模型.利用KDD CUP 1999数据集对模型进行实验测试

并对改进算法的效果进行了对比和分析.实验证明

新的检测系统具有良好的性能.

Abstract

IDS (Intrusion Detection system) is an active and driving defense technology. This paper mainly focuses on intrusion detection based on data mining. The aim is to improve the detection rate and decrease the false alarm rate

and the main research method is clustering analysis. The algorithm and model of ID are proposed and corresponding simulation experiments are presented. Firstly

a method to reduce the noise and isolated points on the data set was advanced. By dividing and merging clusters and using the density radius of super sphere

an algorithm to calculate the number of the Cluster Center was given. By the more accurate method of finding clustering center

an anomaly detection model was presented to get better detection effect. This paper used KDD CUP 1999 data set to test the performance of the model. The results show the system has a higher detection rate and a lower false alarm rate

it achieves expectant aim.

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Related Author

LIU Tao

MA Xiao-yu

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Related Institution

1 School of Communication Engineering, Xi′an University of Science and Technology, Xi′an

2 Shaanxi Rapid Transit Industry and Trade Co., Ltd., Xi′an

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