TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space |
| 영문제목(English Title) |
Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space |
| 저자(Author) |
Hansung Lee
Daesung Moon
Ikkyun Kim
Hoseok Jung
Daihee Park
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| 원문수록처(Citation) |
VOL 09 NO. 03 PP. 1173 ~ 1192 (2015. 03) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
The Support Vector Data Description (SVDD) has achieved great success in anomaly detection, directly finding the optimal ball with a minimal radius and center, which contains most of the target data. The SVDD has some limited classification capability, because the hyper-sphere, even in feature space, can express only a limited region of the target class. This paper presents an anomaly detection algorithm for mitigating the limitations of the conventional SVDD by finding the minimum volume enclosing ellipsoid in the feature space. To evaluate the performance of the proposed approach, we tested it with intrusion detection applications. Experimental results show the prominence of the proposed approach for anomaly detection compared with the standard SVDD.
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| 키워드(Keyword) |
Anomaly detection
intrusion detection
kernel principal component analysis
minimum enclosing ellipsoid
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| 파일첨부 |
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