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Data mining file sharing metadata: A comparison between Random Forests Classification and Bayesian Networks
University of Skövde, School of Informatics.
2015 (English)Independent thesis Basic level (degree of Bachelor), 15 credits / 22,5 HE creditsStudent thesis
Abstract [en]

In this comparative study based on experimentation it is demonstrated that the two evaluated machine learning techniques, Bayesian networks and random forests, have similar predictive power in the domain of classifying torrents on BitTorrent file sharing networks.This work was performed in two steps. First, a literature analysis was performed to gain insight into how the two techniques work and what types of attacks exist against BitTorrent file sharing networks. After the literature analysis, an experiment was performed to evaluate the accuracy of the two techniques.The results show no significant advantage of using one algorithm over the other when only considering accuracy. However, ease of use lies in Random forests’ favour because the technique requires little pre-processing of the data and still generates accurate results with few false positives.

Place, publisher, year, edition, pages
2015. , 43 p.
Keyword [en]
Machine learning, random forests, Bayesian networks, Bit Torrent, file sharing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:his:diva-11285OAI: oai:DiVA.org:his-11285DiVA: diva2:839681
Subject / course
Computer Science
Educational program
Computer Science - Specialization in Systems Development
Supervisors
Examiners
Available from: 2015-07-20 Created: 2015-07-03 Last updated: 2015-07-20Bibliographically approved

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Total: 460 downloads
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CiteExportLink to record
Permanent link

Direct link
Cite
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