Recognition of Similar NetFlow Data in Decentralised Monitoring Environments

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dc.contributor.author Eisenhart, Georg
dc.contributor.author Volpert, Simon
dc.contributor.author Braitinger, Jan
dc.contributor.author Domaschka, Jörg
dc.date.accessioned 2022-04-08T07:51:20Z
dc.date.available 2022-04-08T07:51:20Z
dc.date.issued 2022-04-07
dc.identifier.uri http://hdl.handle.net/10900/126090
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1260905 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-67453
dc.description.abstract One of the main challenges in the analysis of NetFlow data in decentralised monitoring environments comes from merging datasets from different independent sites. One problem is to identify similar data points which can impact derived metrics from such data directly. This article provides a proof of concept how similarity measurements based on distance metrics can be used to identify similar or related flows from different datasets. For this, several domains are outlined which can benefit from this approach to support validation of research scenarios and data analysis. en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.subject.ddc 004 de_DE
dc.title Recognition of Similar NetFlow Data in Decentralised Monitoring Environments en
dc.type ConferencePaper de_DE
utue.publikation.fachbereich Informatik de_DE
utue.publikation.fakultaet 7 Mathematisch-Naturwissenschaftliche Fakultät de_DE
utue.opus.portal kuvs-nets3 de_DE
utue.publikation.noppn yes de_DE

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