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Unsupervised Anomaly- and Outlier Detection in Network Metrics
Eriksson, August
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2020 (English)
Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
Student 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-80471
OAI: oai:DiVA.org:ltu-80471
DiVA, 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
Olov, Schelén
Examiners
Olov, Schelén
Available from:
2020-08-28
Created:
2020-08-19
Last updated:
2020-08-28
Bibliographically approved
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Department of Computer Science, Electrical and Space Engineering
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Computer Sciences
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modern-language-association-8th-edition
vancouver
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modern-language-association-8th-edition
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