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Automated Spyware Detection Using End User License Agreements
Responsible organisation
2008 (English)Conference paper, Published paper (Refereed) PublishedAlternative title
Automatisk detektion av spyware genom klassificering av slutanvändarlicenser (Swedish)
Abstract [en]

The amount of spyware increases rapidly over the Internet and it is usually hard for the average user to know if a software application hosts spyware. This paper investigates the hypothesis that it is possible to detect from the End User License Agreement (EULA) whether its associated software hosts spyware or not. We generated a data set by collecting 100 applications with EULAs and classifying each EULA as either good or bad. An experiment was conducted, in which 15 popular default-configured mining algorithms were applied on the data set. The results show that 13 algorithms are significantly better than random guessing, thus we conclude that the hypothesis can be accepted. Moreover, 2 algorithms also perform significantly better than the current state-of-the-art EULA analysis method. Based on these results, we present a novel tool that can be used to prevent the installation of spyware.

Abstract [sv]

Spridandet av spyware har ökat dramatiskt och det är ofta svårt för användaren att veta om spyware kommer att installeras samtidigt som en nedladdat applikation skall installeras. Den här studien undersöker om det är möjligt att avgöra om en applikation innehåller spyware genom att applicera data mining tekniker på applikationens slutanvändarlicens.

Place, publisher, year, edition, pages
Busan, Korea: IEEE , 2008.
Keyword [en]
eula, classification, data mining, supervised learning
National Category
Computer Sciences
URN: urn:nbn:se:bth-8608ISI: 000256051100085Local ID: diva2:836349
2nd International Conference on Information Security and Assurance
Copyright © 2008 IEEE. Reprinted from the proceedings of the 2nd International Conference on Information Security and Assurance . This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of BTH's products or services Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by sending a blank email message to By choosing to view this document, you agree to all provisions of the copyright laws protecting it.Available from: 2012-09-18 Created: 2008-04-29 Last updated: 2018-01-11Bibliographically approved

Open Access in DiVA

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