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dc.contributor.authorVillar-Rodriguez, Esther
dc.contributor.authorDel Ser, Javier
dc.contributor.authorSalcedo-Sanz, Sancho
dc.date.accessioned2016-12-01T15:58:16Z
dc.date.available2016-12-01T15:58:16Z
dc.date.issued2015
dc.identifier.citationINTELLIGENT DISTRIBUTED COMPUTING VIII, Studies in Computational Intelligence, Volume: 570, 259-268, 10.1007/978-3-319-10422-5_28en
dc.identifier.isbn978-3-319-10421-8en
dc.identifier.issn1860-949Xen
dc.identifier.urihttp://hdl.handle.net/11556/348
dc.description.abstractLately the proliferation of social networks has given rise to a myriad of fraudulent strategies aimed at getting some sort of benefit from the attacked individual. Despite most of them being exclusively driven by economic interests, the so called impersonation, masquerading attack or identity fraud hinges on stealing the credentials of the victim and assuming his/her identity to get access to resources (e.g. relationships or confidential information), credit and other benefits in that person’s name. While this problem is getting particularly frequent within the teenage community, the reality is that very scarce technological approaches have been proposed in the literature to address this issue which, if not detected in time, may catastrophically unchain other fatal consequences to the impersonated person such as bullying and intimidation. In this context, this paper delves into a machine learning approach that permits to efficiently detect this kind of attacks by solely relying on connection time information of the potential victim. The manuscript will demonstrate how these learning algorithms - in particular, support vector classifiers - can be of great help to understand and detect impersonation attacks without compromising the user privacy of social networks.en
dc.description.sponsorshipThe presented work has been partially supported by the Basque Government under the CYBERSID project grant.en
dc.language.isoengen
dc.publisherSPRINGER-VERLAG BERLIN, HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANYen
dc.titleOn a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networksen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-319-10422-5_28en
dc.isiYesen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsImpersonationen
dc.subject.keywordsSocial Networksen
dc.subject.keywordsSupport Vector Machinesen
dc.journal.titleStudies in Computational Intelligenceen
dc.page.final268en
dc.page.initial259en
dc.volume.number570en


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