[1]翟素兰 郑诚.用于入侵检测的基于粗糙集的贝叶斯分类器[J].计算机技术与发展,2006,(01):226-227.
　ZHAI Su-lan,ZHENG Cheng.Bayes Classifier Based on Rough Set Used in Intrusion Detection[J].,2006,(01):226-227.
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用于入侵检测的基于粗糙集的贝叶斯分类器()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2006年01期
页码:
226-227
栏目:
计算机安全
出版日期:
1900-01-01

文章信息/Info

Title:
Bayes Classifier Based on Rough Set Used in Intrusion Detection
文章编号:
1005-3751（2006）01-0226-02
作者:
翟素兰 郑诚
安徽大学计算机科学与技术学院
Author(s):
ZHAI Su-lan;  ZHENG Cheng
School of Computer Science and Technology ,Anhui University
关键词:
入侵检测; 朴素贝叶斯; 粗糙集; 属性约简
Keywords:
intrusion detection system;  naive bayes;  rough; set ; festure reduction
分类号:
TP393.08
文献标志码:
A
摘要:
网络安全的同题日趋严重，人佟检测的研究是当今的研究热点。将数据挖掘和机器学习技术用于入侵检测是一个可行的方法。有很多算法用于入侵检测中，但有的是正确率比较低，也有的是学习或分类时间长，这些都限制了入侵检测系统在实际中的应用。文中提出了将粗糙集用于网络侦听的海量数据的属性约简，而后提出使用朴索贝叶斯进行分类预测。该方法的准确率高，而且时间性能好，适用于网络人侵检测的要求
Abstract:
The technology of data minging and machine learning has been used in intrusion detection. The algorithm used in IDS needs that the accurate rate is high and the time of learming or classifying is short. Yet, lots of algorithms used in IDS cannot meet the needs which limit the use of IDS in pratice . In the paper,the naive hayes classifier based rough set reduction is proposed to use in IDS. The structure of naive hayes is simple,and learning corret efficiency and time efficiency is perfect. But it needs the independence of feature, which can be achieved by reduction based on rough set. It is fit for intrusion decahedron

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备注/Memo

备注/Memo:
安徽省教育厅自然基金资助项目（2002kj009）翟素兰（1977-），女，安徽涡阳人，硕士研究生，研究方向为数据挖掘、网络安全
更新日期/Last Update: 1900-01-01