Mining maximal frequent itemsets for intrusion detection

被引：0

作者：

Wang, H ^{[1
]}

Li, QH ^{[1
]}

Xiong, HY ^{[1
]}

Jiang, SY ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Comp Sch, Wuhan 430074, Peoples R China

来源：

GRID AND COOPERATIVE COMPUTING GCC 2004 WORKSHOPS, PROCEEDINGS | 2004年 / 3252卷

关键词：

data mining; intrusion detection; maximal frequent itemset;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

It has been the recent research focus and trend to apply data mining techniques in an intrusion detection system for discovering new types of attacks, but it is still in its infancy. This paper presents an innovative technique, called MMID, that applies maximal frequent itemsets mining to intrusion detection and can significantly improve the accuracy and performance of an intrusion detection system. The experimental results show that MMID is efficient and accurate for the attacks that occur intensively in a short period of time.

引用

页码：422 / 429

页数：8

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