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

Villar-Rodriguez, EstherDel Ser, JavierSalcedo-Sanz, Sancho2015Villar-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_281860-949Xresearchoutputwizard: 11556/348Publisher Copyright: © Springer International Publishing Switzerland 2015.Lately 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.10312252enginfo:eu-repo/semantics/restrictedAccessOn a Machine Learning Approach for the Detection of Impersonation Attacks in Social Networksjournal article10.1007/978-3-319-10422-5_28ImpersonationSocial NetworksSupport Vector MachinesImpersonationSocial NetworksSupport Vector MachinesArtificial IntelligenceFunding InfoThe presented work has been partially supported by the Basque Government under the CYBERSID project grant.The presented work has been partially supported by the Basque Government under the CYBERSID project grant.http://www.scopus.com/inward/record.url?scp=84921657437&partnerID=8YFLogxK