摘要
把基于数据垂直分布的模糊关联规则挖掘算法引入到网络的入侵检测,利用该算法从网络数据集中对采集到的数据进行模糊化的处理,并将数据垂直分布于位图中.利用k-means聚类算法建立属性的模糊集和模糊隶属函数,该算法克服了传统的离散分区法的不足,同时改进了已有模糊关联规则,提取出具有较高可信性和完备性的模糊关联规则.
This paper employs fuzzy association rules based on vertical data to intrusion detection of network,and uses this algorithm for complying with the data from network,and makes it into fuzzification.The data of vertical distribution are put into a bitmap.Making use of K-means cluster algorithm to build blurred collection and function.This algorithm not only overcomes the deficiency of the traditional discrete segmentation method,but also improves fuzzy association rules and provides highly effective fuzzy rules.
出处
《哈尔滨师范大学自然科学学报》
CAS
2010年第3期65-67,共3页
Natural Science Journal of Harbin Normal University
关键词
数据垂直分布
模糊关联规则
入侵检测
Vertical data
Fuzzy association rules
Intrusion detection
