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Unsupervised Anomaly- and Outlier Detection in Network Metrics
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2020 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Place, publisher, year, edition, pages
2020. , p. 57
Keywords [en]
Anomaly detection, outlier detection, ARIMA, DBSCAN, LSTM, Au-toencoder, moving average, network metric analysis
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-80471OAI: oai:DiVA.org:ltu-80471DiVA, id: diva2:1459387
External cooperation
Netrounds AB
Subject / course
Student thesis, at least 30 credits
Educational program
Computer Science and Engineering, master's level
Supervisors
Examiners
Available from: 2020-08-28 Created: 2020-08-19 Last updated: 2020-08-28Bibliographically approved

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CiteExportLink to record
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