Visualization of Data Cubes for Anomaly Detection in Network Traffic Data Streams

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Název: Visualization of Data Cubes for Anomaly Detection in Network Traffic Data Streams
Autoři: Ahlers, Volker, Laue, Tim, Wellermann, Nils, Heine, Felix
Zdroj: 2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). :272-277
Informace o vydavateli: IEEE, 2021.
Rok vydání: 2021
Témata: Computersicherheit, Rechnernetz, Benutzeroberfläche, ddc:004, Visualisierung, 004 Informatik
Popis: For anomaly-based intrusion detection in computer networks, data cubes can be used for building a model of the normal behavior of each cell. During inference an anomaly score is calculated based on the deviation of cell metrics from the corresponding normality model. A visualization approach is shown that combines different types of diagrams and charts with linked user interaction for filtering of data.
Druh dokumentu: Article
Conference object
Popis souboru: application/pdf
DOI: 10.1109/idaacs53288.2021.9660978
DOI: 10.25968/opus-2160
Přístupová URL adresa: https://serwiss.bib.hs-hannover.de/files/2160/ahlers_idaacs_2021.pdf
Rights: IEEE Copyright
"In Copyright" Rights Statement
Přístupové číslo: edsair.doi.dedup.....d1b5cfc74a3f15494cc62aef5661d922
Databáze: OpenAIRE
Popis
Abstrakt:For anomaly-based intrusion detection in computer networks, data cubes can be used for building a model of the normal behavior of each cell. During inference an anomaly score is calculated based on the deviation of cell metrics from the corresponding normality model. A visualization approach is shown that combines different types of diagrams and charts with linked user interaction for filtering of data.
DOI:10.1109/idaacs53288.2021.9660978