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Evaluating Performance of Intrusion Detection System using Support Vector Machines : Review
Leila Mohammadpour,Mehdi Hussain,Alihossein Aryanfar,Vahid Maleki Raee,Fahad Sattar 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.9
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.