Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/112071
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dc.contributor.authorTian, H.-
dc.contributor.authorLiu, J.-
dc.contributor.authorDing, M.-
dc.date.issued2017-
dc.identifier.citationComputer Science and Information Systems, 2017; 14(3):597-609-
dc.identifier.issn1820-0214-
dc.identifier.issn2406-1018-
dc.identifier.urihttp://hdl.handle.net/2440/112071-
dc.description.abstractIn various networks, anomaly may happen due to network breakdown, intrusion detection, and end-to-end traffic changes. To detect these anomalies is important in diagnosis, fault report, capacity plan and so on. However, it’s challenging to detect these anomalies with high accuracy rate and time efficiency. Existing works are mainly classified into two streams, anomaly detection on link traffic and on global traffic. In this paper we discuss various anomaly detection methods on both types of traffic and compare their performance.-
dc.description.statementofresponsibilityHui Tian, Jingtian Liu and Meimei Ding-
dc.language.isoen-
dc.publisherComSIS Consortium-
dc.rightsComputer Science and Information Systems is an Open Access journal. All articles can be downloaded free of charge and used in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY NC ND) License.-
dc.source.urihttp://dx.doi.org/10.2298/csis170201018h-
dc.subjectDiffusion wavelet; principal component analysis; anomaly detection-
dc.titlePromising techniques for anomaly detection on network traffic-
dc.typeJournal article-
dc.identifier.doi10.2298/CSIS170201018H-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP150104871-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 8
Computer Science publications

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