Nine years of observing traffic anomalies: Trending analysis in backbone networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Won, Youngjoon | - |
dc.contributor.author | Fontugne, Romain | - |
dc.contributor.author | Cho, Kenjiro | - |
dc.contributor.author | Esaki, Hiroshi | - |
dc.contributor.author | Fukuda, Kensuke | - |
dc.date.accessioned | 2022-07-16T10:03:01Z | - |
dc.date.available | 2022-07-16T10:03:01Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2013-05 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/162827 | - |
dc.description.abstract | We present the longitudinal trending analysis of traffic anomalies on a trans-Pacific backbone network over nine years. Throughout our analysis, we try to answer several questions: how frequent do such anomalies appear and how long do they last? Does a set of anomalous hosts occur correspondingly? We answer these by applying the state-of-the-art anomaly detectors to (un)anonymized packet traces and look into interesting insights from the long-term analysis. The key observations are as follow. The sources of anomalies are decreasing over the recent years, but take a significant portion of traffic volume during the measurement period (i.e., 0.03% of all IP addresses take upto 30% of traffic volume). The frequency analysis reveals that there is a clear periodicity of anomalies and anomalous host occurrences in various durations. Finally, we find the influences of anomaly detectors to the overall trending and how they differ from each other. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Nine years of observing traffic anomalies: Trending analysis in backbone networks | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Won, Youngjoon | - |
dc.identifier.scopusid | 2-s2.0-84883467384 | - |
dc.identifier.bibliographicCitation | Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management, IM 2013, pp.636 - 642 | - |
dc.relation.isPartOf | Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management, IM 2013 | - |
dc.citation.title | Proceedings of the 2013 IFIP/IEEE International Symposium on Integrated Network Management, IM 2013 | - |
dc.citation.startPage | 636 | - |
dc.citation.endPage | 642 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Anomaly detector | - |
dc.subject.keywordPlus | Back-bone network | - |
dc.subject.keywordPlus | Frequency Analysis | - |
dc.subject.keywordPlus | IP addresss | - |
dc.subject.keywordPlus | Long term analysis | - |
dc.subject.keywordPlus | Packet traces | - |
dc.subject.keywordPlus | Traffic anomalies | - |
dc.subject.keywordPlus | Traffic volumes | - |
dc.subject.keywordPlus | Network management | - |
dc.identifier.url | https://ieeexplore.ieee.org/abstract/document/6573044 | - |
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