An Improved Anomaly Detection Method Based on Fuzzy Association Rules

被引：0

作者：

Yang, Zifen ^{[1
]}

机构：

[1] Linyi Normal Univ, Sch Informat, Linyi, Peoples R China

来源：

PROCEEDINGS OF THE 2011 INTERNATIONAL CONFERENCE ON INFORMATICS, CYBERNETICS, AND COMPUTER ENGINEERING (ICCE2011), VOL 1: INTELLIGENT CONTROL AND NETWORK COMMUNICATION | 2011年 / 110卷 / 01期

关键词：

fuzzy association; hash list; anomaly detection;

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：

The discovery of association rule is a crucial aspect in database mining. In order to further improve the ability of fuzzy association mining in identifying the unknown intrusion data in anomaly detection, an improved algorithm called Hapriori using hash list on mining fuzzy association rules is proposed in this paper. Equivalence classes are used to search frequent items, so that the usual practice of reputedly database scanning can be avoided. At last. experimental results show that our proposed method to some extent increased recognition of the intrusion data.

引用

页码：441 / 447

页数：7