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Anomaly Detection in A Multivariate DataStream in a Highly Scalable and Fault Tolerant Architecture
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The process of monitoring telecommunication systems performance by investigatingKey Performance Indicators (KPI) and Performance Measurements(PMs) is crucial for valuable sustainable solutions and requires analysts' interventionwith profound knowledge to help mitigate vulnerabilities and risks.This work focuses on PMs anomaly detection in order to automate the processof discovering unacceptable Radio Access Network (RAN) performance byleveraging K-meansjj algorithm and producing an anomaly scoring mechanism.It also oers a streaming, fault tolerant, scalable and loosely coupled architectureto process data on the y based on a normal behavior model. Theproposed architecture is used to test the anomaly scoring system where variousdata patterns are ingested. The tests focused on inspecting the anomaly score'sconsistency, variability and sensitivity. The results were highly impacted by thereal-time standardization process of data, and the scores were not entirely sensitiveto changes in constant features; however, the experiment yielded acceptableresults when the correlation between features was taken into account.

Abstract [sv]

Processen att övervaka telekommunikationssystems utförande genom att undersöka Key Performance Indicators (KPI) och Performance Measurements (PMs)är helt avgörande för värdefulla hållbara lösningar och kräver intervention avdataanalytiker med djup kunskap för att mildra sårbarheter och risker. Detta arbetefokuserar på att upptäcka PMs-anomali för att automatisera processen attupptäcka oacceptabelt utförande hos Radio Access Network genom att användaK-means algoritmen och producerar en mekanism för poäng ge anomalier. Deterbjuder även en strömmande, feltolerant, skalbar och löst bunden arkitektur föratt behandla data i realtid baserat på en normalbeteendemodell. Den föreslagnaarkitekturen används för att testa poängsystemet för anomalier som matas medolika datamönster. Testet fokuserade på att granska anomalinpoängens stabilitet,variation och känslighet. Resultaten påverkades till stor del av realtidsstandardiseringsprocessenav data, och de var inte fullt känsliga förändringari konstanta funktioner; Experimentet gav emellertid godtagbara resultat närkorrelationen mellan funktioner beaktades.

Place, publisher, year, edition, pages
2017. , p. 32
Series
TRITA-EE, ISSN 1653-5146 ; 2017:177
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220982OAI: oai:DiVA.org:kth-220982DiVA, id: diva2:1172847
External cooperation
Ericsson AB
Educational program
Master of Science - Network Services and Systems
Available from: 2018-01-11 Created: 2018-01-11 Last updated: 2018-01-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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