Recognition of Similar NetFlow Data in Decentralised Monitoring Environments

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URI: http://hdl.handle.net/10900/126090
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1260905
http://dx.doi.org/10.15496/publikation-67453
Dokumentart: InProceedings (Aufsatz / Paper einer Konferenz etc.)
Date: 2022-04-07
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
DDC Classifikation: 004 - Data processing and computer science
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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.

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