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Detecting Anomalies in User Communication in an E-commerce Application: Applying a Clustering Algorithm to VectorizedText Messages
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Many applications that allow text based communication between users are troubled with malicious content. This thesis presents a system for detecting such behaviour in an E-commerce application. The system is based on an algorithm for anomaly detection which is trained using messages sent between users in the application. Preprocessing of the text is performed using the NLP-toolbox Glove. The resulting word embeddings are used to create numerical representations of messages, which are then used as input to a clustering algorithm based on K-means. Vectors positioned far away from existing clusters were considered anomalies. This report assesses performance of this system, and relates this to the performance achieved with an existing approach.

Place, publisher, year, edition, pages
2019. , p. 45
Series
UPTEC IT, ISSN 1401-5749 ; 19019
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-395255OAI: oai:DiVA.org:uu-395255DiVA, id: diva2:1361486
Educational program
Master of Science Programme in Information Technology Engineering
Supervisors
Examiners
Available from: 2019-10-16 Created: 2019-10-16 Last updated: 2019-10-16Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • 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
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