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Evaluating Machine Learning Intrusion Detection System classifiers: Using a transparent experiment approach
University of Skövde, School of Informatics.
University of Skövde, School of Informatics.
University of Skövde, School of Informatics.
2019 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

There have been many studies performing experiments that showcase the potential of machine learning solutions for intrusion detection, but their experimental approaches are non-transparent and vague, making it difficult to replicate their trained methods and results. In this thesis we exemplify a healthier experimental methodology.

A survey was performed to investigate evaluation metrics. Three experiments implementing and benchmarking machine learning classifiers, using different optimization techniques, were performed to set up a frame of reference for future work, as well as signify the importance of using descriptive metrics and disclosing implementation.

We found a set of metrics that more accurately describes the models, and we found guidelines that we would like future researchers to fulfill in order to make their work more comprehensible. For future work we would like to see more discussion regarding metrics, and a new dataset that is more generalizable.

Place, publisher, year, edition, pages
2019. , p. 87
Keywords [en]
Network Intrusion Detection System, Intrusion Detection System, Machine Learning, Guidelines, Transparency
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:his:diva-17192OAI: oai:DiVA.org:his-17192DiVA, id: diva2:1327058
Subject / course
Computer Science
Educational program
Computer Science - Specialization in Systems Development
Supervisors
Examiners
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-06-19Bibliographically 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
  • rtf