A model of evolving intrusion detection system based on data mining and immune principle

被引：0

作者：

Zhao, JZ ^{[1
]}

Xu, MZ ^{[1
]}

Sun, SL ^{[1
]}

You, L ^{[1
]}

机构：

[1] Beijing Univ Aeronaut & Astronaut, Sch Sci, Beijing 100083, Peoples R China

来源：

TENCON 2004 - 2004 IEEE REGION 10 CONFERENCE, VOLS A-D, PROCEEDINGS: ANALOG AND DIGITAL TECHNIQUES IN ELECTRICAL ENGINEERING | 2004年

关键词：

computer network; computer security; intrusion detection; data mining; artificial immune system;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, an IDS framework based on Data mining technique and immune principle is presented. Here Data mining technique is used to discover frequently occurred patterns, which are equivalent to self proteins in immune system. Immune principle is explored to generate negative detectors, which does not math any self protein based on distance metric. These negative detectors are distributed into the network system to perform anomaly detection independently and concurrently. Our experiment shows that it has low false positive rate and high detection rate.

引用

页码：B199 / B202

页数：4

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