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Behavioral Monitoring on Smartphones for Intrusion Detection in Web Systems: A Study of Limitations and Applications of Touchscreen Biometrics
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Bevakning av användarbeteende på mobila enheter för identifiering av intrång i webbsystem (Swedish)
Abstract [en]

Touchscreen biometrics is the process of measuring user behavior when using a touchscreen, and using this information for authentication. This thesis uses SVM and k-NN classifiers to test the applicability of touchscreen biometrics in a web environment for smartphones. Two new concepts are introduced: model training using the Local Outlier Factor (LOF), as well as building custom models for touch behaviour in the context of individual UI components instead of the whole screen. The lowest error rate achieved was 5.6 \% using the k-NN classifier, with a standard deviation of 2.29 \%. No real benefit using the LOF algorithm in the way presented in this thesis could be found. It is found that the method of using contextual models yields better performance than looking at the entire screen. Lastly, ideas for using touchscreen biometrics as an intrusion detection system is presented.

Abstract [sv]

Pekskärmsbiometri innebär att mäta beteende hos en användare som använder en pekskärm och känna denna baserat på informationen. I detta examensarbete används SVM och k-NN klassifierare för att testa tillämpligheten av denna typ av biometri i en webbmiljö för smarttelefoner. Två nya koncept introduceras: modellträning med ''Local Outlier Factor'' samt att bygga modeller för användarinteraktioner med enskilda gränssnittselement iställer för skärmen i sin helhet. De besta resultaten för klassifierarna hade en felfrekvens på 5.6 \% med en standardavvikelse på 2.29 \%. Ingen fördel med användning av LOF för träning framför slumpmässig träning kunde hittas. Däremot förbättrades resultaten genom att använda kontextuella modeller. Avslutande så presenteras idéer för hur ett system som beskrivet kan användas för att upptäcka intrång i webbsystem.

Place, publisher, year, edition, pages
2015.
Keyword [en]
Biometrics, Security, Machine Learning, Web Technologies, JavaScript, SVM
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-178077OAI: oai:DiVA.org:kth-178077DiVA: diva2:877205
External cooperation
Valtech AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2015-12-07 Created: 2015-12-06 Last updated: 2015-12-07Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
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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