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Visualization of data cubes for anomaly detection in network traffic data streams

  • 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.

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Metadaten
Author:Volker AhlersORCiDGND, Tim Laue, Nils Wellermann, Felix HeineORCiDGND
URN:urn:nbn:de:bsz:960-opus4-21606
DOI:https://doi.org/10.25968/opus-2160
DOI original:https://doi.org/10.1109/IDAACS53288.2021.9660978
ISBN:978-1-6654-2605-3
ISSN:2770-4254
Parent Title (English):2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Publishing Institution:Hochschule Hannover
Release Date:2022/01/24
Tag:Datenwürfel
Data Cubes; Information Visualization; Multidimensional Analysis; Network Security; User Interfaces
GND Keyword:Computersicherheit; Rechnernetz; Benutzeroberfläche; Visualisierung
First Page:272
Last Page:277
Link to catalogue:1795490780
Institutes:Fakultät IV - Wirtschaft und Informatik
DDC classes:004 Informatik
Licence (German):License LogoUrheberrechtlich geschützt