Ensemble SVM classifiers based on PCA and LDA for IDS

被引：0
|
作者：
Aburomman, Abdulla Amin [1 ]
Reaz, Mamun Bin Ibne [1 ]
机构：
[1] Natl Univ Malaysia, Dept Elect Elect & Syst Engn, Bangi 43600, Selangor, Malaysia
来源：
2016 INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRICAL, ELECTRONIC AND SYSTEMS ENGINEERING (ICAEES) | 2016年
关键词：
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Feature extraction addresses the problem of finding the most compact and informative set of features. To maximize the effectiveness of each single feature extraction algorithm and to develop an efficient intrusion detection system, an ensemble of Linear Discriminant Analysis and Principle Component Analysis feature extraction algorithms is implemented. The experiments demonstrate that the ensemble method outperforms single feature extraction method with 92% in overall accuracy.
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页码：95 / 99
页数：5
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