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dc.contributor.author채기준-
dc.contributor.author김미희-
dc.date.accessioned2018-06-02T08:14:14Z-
dc.date.available2018-06-02T08:14:14Z-
dc.date.issued2004-
dc.identifier.issn0302-9743-
dc.identifier.otherOAK-17676-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/244046-
dc.description.abstractRecently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not provide effective defense against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. It is necessary to analyze the fundamental features of DDoS attacks because these attacks can easily vary the used port/protocol, or operation method. In this paper, we propose a combined data mining approach for modeling the traffic pattern of normal and diverse attacks. This approach uses the automatic feature selection mechanism for selecting the important attributes. And the classifier is built with the theoretically selected attribute through the neural network. And then, our experimental results show that our approach can provide the best performance on the real network, in comparison with that by heuristic feature selection and any other single data mining approaches. © Springer-Verlag Berlin Heidelberg 2004.-
dc.languageEnglish-
dc.titleA combined data mining approach for DDoS attack detection-
dc.typeArticle-
dc.relation.volume3090-
dc.relation.indexSCOPUS-
dc.relation.startpage943-
dc.relation.lastpage950-
dc.relation.journaltitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.identifier.scopusid2-s2.0-33745922681-
dc.author.googleKim M.-
dc.author.googleNa H.-
dc.author.googleChae K.-
dc.author.googleBang H.-
dc.author.googleNa J.-
dc.contributor.scopusid채기준(7102584247)-
dc.date.modifydate20180601100020-
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엘텍공과대학 > 컴퓨터공학과 > Journal papers
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