Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hansung | - |
dc.contributor.author | Moon, Daesung | - |
dc.contributor.author | Kim, Ikkyun | - |
dc.contributor.author | Jung, Hoseok | - |
dc.contributor.author | Park, Daihee | - |
dc.date.accessioned | 2021-09-04T18:01:44Z | - |
dc.date.available | 2021-09-04T18:01:44Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2015-03-31 | - |
dc.identifier.issn | 1976-7277 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/94078 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KSII-KOR SOC INTERNET INFORMATION | - |
dc.title | Anomaly Intrusion Detection Based on Hyper-ellipsoid in the Kernel Feature Space | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Park, Daihee | - |
dc.identifier.doi | 10.3837/tiis.2015.03.019 | - |
dc.identifier.scopusid | 2-s2.0-84926347308 | - |
dc.identifier.wosid | 000353109800019 | - |
dc.identifier.bibliographicCitation | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.9, no.3, pp.1173 - 1192 | - |
dc.relation.isPartOf | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | - |
dc.citation.title | KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS | - |
dc.citation.volume | 9 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 1173 | - |
dc.citation.endPage | 1192 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002074962 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Telecommunications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Telecommunications | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | intrusion detection | - |
dc.subject.keywordAuthor | kernel principal component analysis | - |
dc.subject.keywordAuthor | minimum enclosing ellipsoid | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.