文章基本信息

标题：Evaluating Performance of Intrusion Detection System using Support Vector Machines: Review
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作者：Leila Mohammadpour ; Mehdi Hussain ; Alihossein Aryanfar 等
期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2015
卷号：9
期号：9
页码：225-234
DOI：10.14257/ijsia.2015.9.9.20
出版社：SERSC
摘要：The basic task in intrusion detection system is to classify network activities as normal or abnormal while minimizing misclassification. In literature, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) to protect the special computer systems, vulnerable traffics cyber-attacks for computer networks. In addition, Support Vector Machine (SVM) is applied as the classification techniques in literature. However, there is a lack of review for the IDS method using SVM as the classifier. The objective of this paper is to review the contemporary literature and to provide a critical evaluation of various techniques of intrusion detection using SVM as classifier. We analyze and identify the strengths and limitations of various SVM usages as classifier in IDS systems. This paper also highlights the usefulness of SVM in IDS system for network security environment with future direction.
关键词：Intrusion Detection System; Support vector machine; intelligent IDS; ; Machine learning IDS