Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Learning to detect spyware using end user license agreements
Ansvarlig organisasjon
2011 (engelsk)Inngår i: Knowledge and Information Systems, ISSN 0219-1377, Vol. 26, nr 2, s. 285-307Artikkel i tidsskrift (Fagfellevurdert) PublishedAlternativ tittel
Detektion av spionprogram genom inlärning av slutanvändarlicenser (svensk)
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.

sted, utgiver, år, opplag, sider
Springer London , 2011. Vol. 26, nr 2, s. 285-307
Emneord [en]
End user license agreement, Document classification, Spyware
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-7212DOI: 10.1007/s10115-009-0278-zISI: 000286211500005Lokal ID: oai:bth.se:forskinfoB202672BD62D6131C12576BB004218E0OAI: oai:DiVA.org:bth-7212DiVA, id: diva2:834794
Tilgjengelig fra: 2012-11-12 Laget: 2010-01-30 Sist oppdatert: 2018-01-11bibliografisk kontrollert

Open Access i DiVA

fulltekst(194 kB)328 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 194 kBChecksum SHA-512
b517b01767e339ab2d10ca7e9c97a421370f9a473a0119ce5ed4d143cb57f5a5d6c8accde36e5b1b470a6458b453262acbccae1e1a8e9990e5bbe88dee476a3f
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekst

Personposter BETA

Lavesson, NiklasBoldt, Martin

Søk i DiVA

Av forfatter/redaktør
Lavesson, NiklasBoldt, Martin

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 328 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 205 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf