On a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networks

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dc.contributor.authorVillar-Rodriguez, Esther
dc.contributor.authorDel Ser, Javier
dc.contributor.authorSalcedo-Sanz, Sancho
dc.contributor.institutionTecnalia Research & Innovation
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.issued2015
dc.descriptionPublisher Copyright: © Springer International Publishing Switzerland 2015.
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.statusPeer reviewed
dc.format.extent10
dc.format.extent312252
dc.identifier.citationVillar-Rodriguez , E , Del Ser , J & Salcedo-Sanz , S 2015 , ' On a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networks ' , Studies in Computational Intelligence , vol. 570 , pp. 259-268 . https://doi.org/10.1007/978-3-319-10422-5_28
dc.identifier.doi10.1007/978-3-319-10422-5_28
dc.identifier.issn1860-949X
dc.identifier.otherresearchoutputwizard: 11556/348
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84921657437&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofStudies in Computational Intelligence
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsImpersonation
dc.subject.keywordsSocial Networks
dc.subject.keywordsSupport Vector Machines
dc.subject.keywordsImpersonation
dc.subject.keywordsSocial Networks
dc.subject.keywordsSupport Vector Machines
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsFunding Info
dc.subject.keywordsThe presented work has been partially supported by the Basque Government under the CYBERSID project grant.
dc.subject.keywordsThe presented work has been partially supported by the Basque Government under the CYBERSID project grant.
dc.titleOn a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networksen
dc.typejournal article
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