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학술지 Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space
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저자
이한성, 문대성, 김익균, 정호석, 박대희
발행일
201503
출처
KSII Transactions on Internet and Information Systems, v.9 no.3, pp.1173-1192
ISSN
1976-7277
출판사
한국인터넷정보학회
DOI
https://dx.doi.org/10.3837/tiis.2015.03.019
협약과제
14MS2300, 다중소스 데이터의 Long-term History 분석기반 사이버 표적공격 인지 및 추적기술 개발, 김익균
초록
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.
키워드
Anomaly detection, Intrusion detection, Kernel principal component analysis, Minimum enclosing ellipsoid
KSP 제안 키워드
Anomaly detection algorithm, Anomaly intrusion detection, Feature space, Hyper-ellipsoid, Intrusion detection applications, Kernel principal component analysis(KPCA), Minimum volume enclosing ellipsoid(MVEE), Support Vector Data Description, Target data