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http://dx.doi.org/10.5391/JKIIS.2003.13.2.237

Adaptive Intrusion Detection System Based on SVM and Clustering  

Lee, Han-Sung (고려대학교 컴퓨터정보학과)
Im, Young-Hee (대전대학교 컴퓨터정보통신공학부)
Park, Joo-Young (고려대학교 제어계측공학과)
Park, Dai-Hee (고려대학교 컴퓨터정보학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.13, no.2, 2003 , pp. 237-242 More about this Journal
Abstract
In this paper, we propose a new adaptive intrusion detection algorithm based on clustering: Kernel-ART, which is composed of the on-line clustering algorithm, ART (adaptive resonance theory), combining with mercer-kernel and concept vector. Kernel-ART is not only satisfying all desirable characteristics in the context of clustering-based IDS but also alleviating drawbacks associated with the supervised learning IDS. It is able to detect various types of intrusions in real-time by means of generating clusters incrementally.
Keywords
intrusion detection; ART; mercer kernel; concept vector;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Jack Marin, Daniel Ragsdale, and John Shurdu, "A hybrid approach to the profile creation and intrusion detection", Proceedings of DARPA Information Suroivahility Coriference and Exposition, IEEE, 2001.
2 Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001.1.
3 KDD CUP 1999 DATA, Available in http://kdd.ics.uci.edu/databases/kddcup99/kddcup99. html and http://www-cse.ucsd.edu/users/elkan/kdresults.htmI
4 Jianxiong Luo and Susan M. Bridges, "Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection", International Journal of Intelligent Systems, vol. 15, pp. 687-703, 2000.   DOI   ScienceOn
5 Mark Girolami, "Mercer kernel based clustering in feature space", IEEE Transactions on Neural Networks, vol. 13, no. 4, pp. 780-784, 2002.   DOI   ScienceOn
6 Leonid Portnoy, Eleazar Eskin, and Salvatore J. Stolfo. "Intrusion detection with unlabeled data using clustering", Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001), Philadelphia, PA: November 5-8, 2001.
7 Nong Ye and Xiangyang Li, "A scalable clustering technique for intrusion signature recognition", 2001 IEEE Man Systems and Cybernetics Iriformation Assurance Workshop, West Point, NY, June 5-6, 2001.
8 Nello Cristianini and John Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University PRESS, 2000.
9 유신근, 이남훈, 심영철, "침입탐지시스템 평가 방법론" 한국정보처리학회 논문집, vol. 7, no. 11, pp. 3445-3461, 2000.   과학기술학회마을
10 A. Baraldi and E. Chang, "Simplified ART : A new class of ART algorithms", International Computer Science Institute, TR 98-004, 1998.
11 Wenke Lee, Salvatore J. Stolfo, and Kui W. Mok, "A data mining framework for building intrusion detection models", Proceedings of the 1999 IEEE Symposium on Security and Privacy, pp. 120-132, 1999.
12 I. S. Dhillon and D. S. Modha, "Concept decomposition for large sparse text data using clustering", Technical Report RJ 10147(95022), IBM Almaden Research Center, 1999.
13 Results of the KDD '99 Classifier Learning Contest, Available in http://www-cse.ucsd.edu/users/elkan/clresults.html