Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion

ZHANG Rui-xia; WANG Yong

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Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion

更新时间：2026-09-04

- Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion
- Vol. 27, Issue 4, Pages: 190-192(2010)
- 作者机构：
  
  桂林电子科技大学机构计算机与控制学院,广西,桂林,541004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2010
- 稿件说明：
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张瑞霞, 王勇. 基于主成分分析和多分类器融合的入侵检测[J]. 微电子学与计算机, 2010, 27(4): 190-192,197.

ZHANG Rui-xia, WANG Yong. Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion[J]. 2010, 27(4): 190-192.
张瑞霞, 王勇. 基于主成分分析和多分类器融合的入侵检测[J]. 微电子学与计算机, 2010, 27(4): 190-192,197. DOI：

ZHANG Rui-xia, WANG Yong. Intrusion Detection Methods Based on PCA and Multi-Classifier Fusion[J]. 2010, 27(4): 190-192. DOI：

摘要

提出了利用主成分分析 (PCA) 提取入侵特征的多分类器融合的入侵检测算法.首先

利用PCA分类提取入侵子特征

然后通过KNN分类器给出初步的识别结果

最后采用D-S证据理论对识别结果进行融合

得出最终识别结果.通过在KDDCUP’99的标准入侵检测数据集上的实验表明

该方法提高了入侵检测的整体性能.

Abstract

An intrusion detection method using multi-classifier fusion based on Principal Component Analysis (PCA) feature analysis is presented. Firstly

PCA is applied to network intrusion feature extraction. then

initial intrusion detection result is done by three KNN classifiers. Next

the last step is form final result by fusing these results using the D-S evidence theory. Experiment has been done on dataset in KDD-99 and the results show that the intrusion detection performances can be improved by fusion three classifiers.

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