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Detecting Contextual Network Anomaly in the Radio Network Controller from Bayesian Data Analysis
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis presents Bayesian approach for a contextual network anomaly detection. Network anomaly detection is important in a computer system performance monitoring perspective. Detecting a contextual anomaly is much harder since we need to take the context into account in order to explain whether it is normal or abnormal. The main idea of this thesis is to find contextual attributes from a set of indicators, then to estimate the resource loads through the Bayesian model. The proposed algorithm offers three advantages. Firstly, the model can estimate resource loads with automatically selected indicators and its credible intervals. Secondly, both point and collective contextual anomalies can be captured by the posterior predictive distribution. Lastly, the structural interpretation of the model gives us a way to find similar nodes. This thesis employs real data from Radio Network Controller (RNC) to validate the effectiveness in detecting contextual anomalies.

Place, publisher, year, edition, pages
2015.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-180442OAI: oai:DiVA.org:kth-180442DiVA: diva2:893861
Supervisors
Examiners
Available from: 2016-01-13 Created: 2016-01-13 Last updated: 2016-01-13Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf