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Open AccessExtended Abstract

Network Data Unsupervised Clustering to Anomaly Detection

CITIC, UDC, Campus de Elviña s/n, 15071 A Coruña, Spain
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Presented at the XoveTIC Congress, A Coruña, Spain, 27--28 September 2018.
Proceedings 2018, 2(18), 1173; https://doi.org/10.3390/proceedings2181173
Published: 17 September 2018
(This article belongs to the Proceedings of XoveTIC Conference)
In these days, organizations rely on the availability and security of their communication networks to perform daily operations. As a result, network data must be analyzed in order to provide an adequate level of security and to detect anomalies or malfunctions in the systems. Due to the increase of devices connected to these networks, the complexity to analyze data related to its communications also grows. We propose a method, based on Self-Organized Maps, which combine numerical and categorical features, to ease communication network data analysis. Also, we have explored the possibility of using different sources of data.
Keywords: Self-Organizing Maps; IDS; network security; categorical SOM; visualization; unsupervised clustering Self-Organizing Maps; IDS; network security; categorical SOM; visualization; unsupervised clustering
MDPI and ACS Style

López-Vizcaíno, M.; Dafonte, C.; Nóvoa, F.J.; Garabato, D.; Álvarez, M.A. Network Data Unsupervised Clustering to Anomaly Detection. Proceedings 2018, 2, 1173.

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