Change search
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
Learning to detect spyware using end user license agreements
School of Computing, Blekinge Institute of Technology, Ronneby, Sweden.ORCID iD: 0000-0002-0535-1761
School of Computing, Blekinge Institute of Technology, Ronneby, Sweden.
School of Computing, Blekinge Institute of Technology, Ronneby, Sweden.
School of Technology, Malmö University, Malmö, Sweden.
2011 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 26, no 2, p. 285-307Article in journal (Refereed) Published
Abstract [en]

The amount of software that hosts spyware has increased dramatically. To avoid legal repercussions, the vendors need to inform users about inclusion of spyware via end user license agreements (EULAs) during the installation of an application. However, this information is intentionally written in a way that is hard for users to comprehend. We investigate how to automatically discriminate between legitimate software and spyware associated software by mining EULAs. For this purpose, we compile a data set consisting of 996 EULAs out of which 9.6% are associated to spyware. We compare the performance of 17 learning algorithms with that of a baseline algorithm on two data sets based on a bag-of-words and a meta data model. The majority of learning algorithms significantly outperform the baseline regardless of which data representation is used. However, a non-parametric test indicates that bag-of-words is more suitable than the meta model. Our conclusion is that automatic EULA classification can be applied to assist users in making informed decisions about whether to install an application without having read the EULA. We therefore outline the design of a spyware prevention tool and suggest how to select suitable learning algorithms for the tool by using a multi-criteria evaluation approach.

Place, publisher, year, edition, pages
Springer, 2011. Vol. 26, no 2, p. 285-307
Keywords [en]
End user license agreement, Document classification, Spyware
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-37944DOI: 10.1007/s10115-009-0278-zISI: 000286211500005OAI: oai:DiVA.org:hj-37944DiVA, id: diva2:1159933
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(194 kB)8 downloads
File information
File name FULLTEXT01.pdfFile size 194 kBChecksum SHA-512
b517b01767e339ab2d10ca7e9c97a421370f9a473a0119ce5ed4d143cb57f5a5d6c8accde36e5b1b470a6458b453262acbccae1e1a8e9990e5bbe88dee476a3f
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Lavesson, Niklas

Search in DiVA

By author/editor
Lavesson, Niklas
In the same journal
Knowledge and Information Systems
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 8 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 23 hits
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