Volume 5 Number 3 (Mar. 2010)
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JSW 2010 Vol.5(3): 328-335 ISSN: 1796-217X
doi: 10.4304/jsw.5.3.328-335

Research and Application of an Improved Support Vector Clustering Algorithm on Anomaly Detection

Sheng Sun1, 2, YuanZhen Wang1
1School of Computer Science, Huazhong University of Science and Technology, Wuhan, China
2School of Computer Science, Huangshi Institute of Technology, Huangshi, China

Abstract—Anomaly detection approaches build models of normal data and detect deviations from the normal model in observed data. Anomaly detection applied to intrusion detection and computer security has been an active area of research. The major benefit of anomaly detection algorithms is their ability to potentially detect unforeseen attacks. In this paper, a novel weighted support vector clustering algorithm for anomaly detection is proposed. The weight to each input point is defined according to the position of samples in sphere space. The results of experiment demonstrate that the algorithm has excellent capability and applying it in intrusion detection system can be an effective way via using the data sets of KDD cup 99.

Index Terms—clustering; support vector clustering; anomaly detection; outlier.


Cite: Sheng Sun, YuanZhen Wang, "Research and Application of an Improved Support Vector Clustering Algorithm on Anomaly Detection," Journal of Software vol. 5, no. 3, pp. 328-335, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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