文章基本信息

标题：Feature Selection Technique Applied with Many Data Mining Based Model
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作者：Pratibha Soni ; Prabhakar Sharma
期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2015
卷号：6
期号：1
页码：121-126
出版社：TechScience Publications
摘要：There is wide use of internet for data exchange and increasing rapidly in almost all the domains including online transaction and data communication, Also due to these attacks are increasing rapidly. Also security of information in victim computer is an important need, which requires a security wall for identification and prevention of attacks in form of intrusion detection system (IDS). Basically Intrusion detection system (IDS) is a classifier that can classify the network data as normal or attack. In this research work feature selection technique applied with three classification techniques C5.0, QUEST and ANN to develop the IDS model in five different partition with continuously reducing features. To develop the model KDD99dataset used as benchmark data. Performance of the classification model is measured in terms of accuracy. C5.0 based model with minimum 37 number of feature is producing best accuracy of 99.95%.
关键词：Feature selection; decision tree; partition size