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Investigation of anomalies in a RTC system using Machine Learning
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In a Real Time Clearing System (RTCS) there are several thousands of transactions per second, and even more messages are sent back and forth. Th‘e high volume of messages and transactions being sent within the system eventually leads to some anomalies arising. Th‘is thesis examines how to detect such anomalies with unsupervised Machine Learning models such as, Support Vector Machine(SVM) One Class (OC), Isolation Forest (iForest) and Local Outlier Factor(LOF). Th‘e main objective is to investigate if anomaly detection is useable in Cinnobers RTCS, only using unsupervised models and if they perform at an acceptable level. Th‘e evaluation of the models will be done using a rough labeling method to score them on detection rate, F-score and Ma‹hews correlation coecient (MCC). Th‘e results of the thesis shows that SVM OC is the best model of the three, but requires hyper parameter tuning to perform at an acceptab lelevel so that it may be used for the RTCS without human supervision.

Place, publisher, year, edition, pages
2019. , p. 42
Series
UMNAD ; 1210
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:umu:diva-164768OAI: oai:DiVA.org:umu-164768DiVA, id: diva2:1366924
External cooperation
Cinnober
Educational program
Master of Science Programme in Computing Science and Engineering
Supervisors
Examiners
Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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Output format
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