文章基本信息

标题：Improving the Efficiency of Intrusion Detection Systems Resulted from Combination of Data Mining Method and Machine Learning
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作者：Mostafa Boroumandzadeh ; Ali Shaeidi
期刊名称：International Journal of Computer Science and Network Solutions
印刷版ISSN：2345-3397
出版年度：2014
卷号：2
期号：12
页码：27-32
出版社：International Journal of Computer Science and Network Solutions
摘要：Intrusion detection in a computerized network and the obstacles ahead of it, is the most importantchallenge in security foundations of these networks for increasing higher efficiency of this detection andit is deniable in fact. However, intrusion detection systems, in addition to their software and hardwaremodels and patterns can be influenced by different attacks in spite of automatizing the processes underprocessing in the framework of a warning mechanism, and they can change into a trouble making factorfor different services or final servers. In This paper, we introduced a combined method of categorizedtechniques and characteristic reduction to contrast these attacks and by applying intrusion detectionmethod based on data mining. The results showed that there is a comparison between the performancesof Random Tree algorithm with j48 algorithm. The accuracy Random Tree algorithm reached 96.05%, ahigher percentage than previous methods in this paper
关键词：Intrusion Detection System; Attack; Data mining; Decision tree