A hierarchical intrusion detection model based on the PCA neural networks

被引：92

作者：

Liu, Guisong ^{[1
]}

Yi, Zhang ^{[1
]}

Yang, Shangming ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Compuatat Intelligence Lab, Chengdu 610054, Peoples R China

来源：

NEUROCOMPUTING | 2007年 / 70卷 / 7-9期

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

intrusion detection; principal component analysis; hierarchical detection model; neural networks;

D O I：

10.1016/j.neucom.2006.10.146

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Most of existing intrusion detection (ID) models with a single-level structure can only detect either misuse or anomaly attacks. A hierarchical ID model using principal component analysis (PCA) neural networks is proposed to overcome such shortages. In the proposed model, PCA is applied for classification and,neural networks are used for online computing. Experimental results and comparative studies based on the 1998 DARPA evaluation data sets are given, which show the proposed model can classify the network connections with satisfying performance. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：1561 / 1568

页数：8

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