文章基本信息

标题：Improving the Intrusion Detection Systems' Performance by Correlation as a Sample Selection Method
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作者：Rahimeh Rouhi ; Farshid Keynia ; Mehran Amiri 等
期刊名称：Journal of Computer Sciences and Applications
印刷版ISSN：2328-7268
电子版ISSN：2328-725X
出版年度：2013
卷号：1
期号：3
页码：33-38
DOI：10.12691/jcsa-1-3-1
语种：English
出版社：Science and Education Publishing
摘要：Due to a growing number of the computer networks in recent years, there has been an increasing interest in the intrusion detection systems (IDSs). In this paper we have proposed a method applied to the instance selection from KDD CUP 99 dataset which is used for evaluating the anomaly detection techniques. In order to determine the performance of proposed method in the dataset reduction, a feed forward neural network was trained by a reduced dataset to classify normal or attack records in the dataset. The most obvious finding resulted from this study is a considerable increase in the accuracy rate obtained from the neural network.
关键词：intrusion detection system (IDS); instance selection; anomaly detection; neural network