상세정보
학술지
Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space
- 발행일
- 201503
- 출처
- KSII Transactions on Internet and Information Systems, v.9 no.3, pp.1173-1192
- ISSN
- 1976-7277
- 출판사
- 한국인터넷정보학회
- 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.
- KSP 제안 키워드
- Anomaly detection algorithm, Anomaly intrusion detection, Feature space, Hyper-ellipsoid, Intrusion detection applications, Minimum volume enclosing ellipsoid(MVEE), Support Vector Data Description, Target data