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인공지능대학
컴퓨터공학과
Journal papers
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A combined data mining approach for DDoS attack detection
Title
A combined data mining approach for DDoS attack detection
Authors
Kim M.
;
Na H.
;
Chae K.
;
Bang H.
;
Na J.
Ewha Authors
채기준
;
김미희
SCOPUS Author ID
채기준
Issue Date
2004
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 3090, pp. 943 - 950
Indexed
SCOPUS
Document Type
Article
Abstract
Recently, 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.
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