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Abnormal pattern detection based on visualization

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dc.contributor.authorJin, A.-S.-
dc.contributor.authorHwang, D.-D.-
dc.contributor.authorKim, G.-Y.-
dc.contributor.authorChang, H.-
dc.contributor.authorLee, S.-
dc.contributor.authorChoi, H.-I.-
dc.date.available2019-04-10T10:58:02Z-
dc.date.created2018-04-17-
dc.date.issued2010-
dc.identifier.isbn9781424455850-
dc.identifier.urihttp://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/33294-
dc.description.abstractMalicious traffics on the internet are difficult to detect among massive network traffic flow. In most existing method about network attack visualization systems, normally, the attacks cannot be automatically detected by the system. In the paper, a new network traffic visualization based on artificial neural network is proposed. The proposed method is capable of detecting attack traffic pattern more easily. In the proposed method, image is firstly made using the source IP, source port, destination IP and destination port, and then patterns are detected by Hough transform. Pattern features are evaluated by artificial neural network, through which abnormal patterns are classified. The performance of the proposed method proved through experiment results. ©2010 IEEE.-
dc.relation.isPartOf2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010-
dc.titleAbnormal pattern detection based on visualization-
dc.typeConference-
dc.identifier.doi10.1109/ICCAE.2010.5451665-
dc.type.rimsCONF-
dc.identifier.bibliographicCitation2nd International Conference on Computer and Automation Engineering, ICCAE 2010, v.4, pp.363 - 366-
dc.description.journalClass2-
dc.identifier.scopusid2-s2.0-77952659609-
dc.citation.conferenceDate2010-02-26-
dc.citation.conferencePlaceSingapore-
dc.citation.endPage366-
dc.citation.startPage363-
dc.citation.title2nd International Conference on Computer and Automation Engineering, ICCAE 2010-
dc.citation.volume4-
dc.contributor.affiliatedAuthorKim, G.-Y.-
dc.contributor.affiliatedAuthorChang, H.-
dc.contributor.affiliatedAuthorLee, S.-
dc.contributor.affiliatedAuthorChoi, H.-I.-
dc.type.docTypeConference Paper-
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College of Information Technology > Global School of Media > 2. Conference Papers
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