文章基本信息

标题：An Intrusion Detection System based on KDD-99 Data using Data Mining Techniques and Feature Selection
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作者：Pratibha Soni ; Prabhakar Sharma
期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2014
卷号：4
期号：3
页码：112-118
出版社：International Journal of Soft Computing & Engineering
摘要：Internet and internet users are increasing day by day. Also due to rapid development of internet technology, security is becoming big issue. Intruders are monitoring computer network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to prevent computer network from attacks. A comprehensive study of literatures proves that data mining techniques are more powerful technique to develop IDS as a classifier. Performance of classifier is a crucial issue in terms of its efficiency, also number of feature to be scanned by the IDS should also be optimized. In this paper two techniques C5.0 and artificial neural network (ANN) are utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of the five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better results with all most 100% accuracy. Performances were also verified in terms of data partition size.
关键词：Decision tree; Feature Selection; Intrusion;Detection System; Partition Size; Performance measures.