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Towards automated and proactive anomaly detection in a fiber access network
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-8102-5773
Lunet, Sweden.
Savantic, Sweden.
Savantic, Sweden.
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2023 (English)In: Proceedings of 18th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2023), Kristianstad, June 14-15, 2023., 2023Conference paper, Published paper (Refereed)
Abstract [en]

Communication networks are vital for society and network availability is therefore crucial. There is a huge potential in using network telemetry data and machine learning algorithms to proactively detect anomalies and remedy problems before they affect the customers. In practice, however, there are many steps on the way to get there. In this paper we present ongoing development work on efficient data collection pipelines, anomaly detection algorithms and analysis of traffic patterns and predictability.

Place, publisher, year, edition, pages
2023.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-67488OAI: oai:DiVA.org:ri-67488DiVA, id: diva2:1801692
Conference
18th Swedish National Computer Networking and Cloud Computing Workshop (SNCNW 2023)
Available from: 2023-10-02 Created: 2023-10-02 Last updated: 2023-10-02Bibliographically approved

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Abrahamsson, Henrik

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CiteExportLink to record
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  • apa
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