Earticle

초록

영어

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.

목차

Abstract
 1. Introduction
 2. Measurement Tools
 3. Literature Review
 4. Critical Analyses
 5. Conclusion
 References

키워드

저자

• Leila Mohammadpour [ Faculty of Computer Science and Information Technology, University of Malaya 50603 Kuala Lumpur, Malaysia ]
• Mehdi Hussain [ Faculty of Computer Science and Information Technology, University of Malaya 50603 Kuala Lumpur, Malaysia, School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad Pakistan ]
• Alihossein Aryanfar [ Faculty of Computer Science and Information Technology, University Putra Malaysia, Malaysia ]
• Vahid Maleki Raee [ Faculty of Computer Science and Information Technology, University of Malaya 50603 Kuala Lumpur, Malaysia ]
• Fahad Sattar [ University of Management and Technology, Lahore Pakistan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605