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Identifiering av anomalier i COSMIC genom analys av loggar
Linköping University, Department of Computer and Information Science.
Linköping University, Department of Computer and Information Science.
2015 (Swedish)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Identification of anomalies in COSMIC through log analysis (English)
Abstract [sv]

Loggar är en viktig del av alla system, det ger en inblick i vad som sker. Att analysera loggar och extrahera väsentlig information är en av de största trenderna nu inom IT-branchen. Informationen i loggar är värdefulla resurser som kan användas för att upptäcka anomalier och hantera dessa innan det drabbar användaren.

I detta examensarbete dyker vi in i grunderna för informationssökning och analysera undantagsutskrifter i loggar från COSMIC för att undersöka om det är möjligt att upptäcka anomalier med hjälp av retrospektivdata. Detta examensarbete ger även en inblick i möjligheten att visualisera data från loggar och erbjuda en kraftfull sökmotor. Därför kommer vi att fördjupa oss i de tre välkända program som adresserar frågorna i centraliserad loggning: Elasticsearch, Logstash och Kibana.

Sammanfattningsvis visar resultatet att det är möjligt att upptäckta anomalier genom att tillämpa statistiska metoder både på retrospektiv- och realtidsdata.

Abstract [en]

Logs are an important part of any system; it provides an insight into what is happening. One of the biggest trends in the IT industry is analyzing logs and extracting essential information. The information in the logs are valuable resources that can be used to detect anomalies and manage them before it affects the user

In this thesis we will dive into the basics of the information retrieval and analyze exceptions in the logs from COSMIC to investigate whether it is feasible to detect anomalies using retrospective data. This thesis also gives an insight into whether it’s possible to visualize data from logs and offer a powerful search engine. Therefore we will dive into the three well known applications that addresses the issues in centralized logging: Elasticsearch, Logstash and Kibana.

In summary, our results shows that it’s possible to detected anomalies by applying statistical methods on both in retrospective and real time data.

Place, publisher, year, edition, pages
2015. , 56 p.
Keyword [en]
Information retrieval, log management, real-time monitoring, classification, normalization, aggregation, correlation, vector space model, Boolean retrieval, Lucene, Elasticsearch, Logstash, Kibana, database, COSMIC, Cambio, LIPS
Keyword [sv]
Informationssökning, logghantering, realtidsövervakning, klassificering, normalisering, aggregering, korrelation, vektorrumsmodell, boolesk hämtningsmodell, Lucene, Elasticserach, Logstash, Kibana, databaser, COSMIC, Cambio, LIPS
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:liu:diva-123361ISRN: LIU-IDA/LITH-EX-G--15/028--SEOAI: oai:DiVA.org:liu-123361DiVA: diva2:882047
External cooperation
Cambio Healthcare Systems AB
Subject / course
Computer Engineering
Presentation
2015-10-27, Alan Turing, E-Huset, Linköpings universitet, 581 83 Linköping, 13:00 (Swedish)
Supervisors
Examiners
Available from: 2015-12-17 Created: 2015-12-13 Last updated: 2015-12-17Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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  • 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