文章基本信息

标题：Developing Data Mining Techniques for Intruder Detection in Network Traffic
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作者：Amar Agrawal ; Sabah Mohammed ; Jinan Fiaidhi 等
期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2016
卷号：10
期号：8
页码：335-342
出版社：SERSC
摘要：In this paper we have proposed a hybrid intrusion detection system consisting of a misuse detection model based upon a Binary Tree of Classifiers as the first stage and an anomaly detection model based upon SVM Classifier as the second stage. The Binary Tree consists of several best known classifiers specialized in detecting specific attacks at a high level of accuracy. Combination of a Binary Tree and specialized classifiers will increase accuracy of the misuse detection model. The misuse detection model will detect only known attacks. In-order to detect unknown attacks, we have an anomaly detection model as the second stage. SVM has been used, since it’s the best known classifier for anomaly detection which will detect patterns that deviate from normal behavior. The proposed hybrid intrusion detection has been tested and evaluated using KDD Cup ’99, NSL-KDD and UNSW-NB15 dataset.
关键词：Intrusion Detection System; Data Mining; Hybrid IDS