000000307 001__ 307
000000307 005__ 20150814024829.0
000000307 037__ $$aLERSSE-RefConfPaper-2015-005
000000307 100__ $$aYazan Boshmaf
000000307 245__ $$aThwarting Fake OSN Accounts by Predicting their Victims
000000307 260__ $$c2015-07-28
000000307 300__ $$amult. p
000000307 520__ $$aTraditional defense mechanisms for fighting against automated fake accounts in online social networks are victim-agnostic. Even though victims of fake accounts play an important role in the viability of subsequent attacks, there is no work on utilizing this insight to improve the status quo. In this position paper, we take the first step and propose to incorporate predictions about victims of unknown fakes into the workflows of existing defense mechanisms. In particular, we investigated how such an integration could lead to more robust fake account defense mechanisms. We also used real-world datasets from Facebook and Tuenti to evaluate the feasibility of predicting victims of fake accounts using supervised machine learning.
000000307 6531_ $$aFake Accounts
000000307 6531_ $$aOnline Social Networks
000000307 6531_ $$aApplied Machine Learning
000000307 6531_ $$aSocial Botnets
000000307 700__ $$aMatei Ripeanu
000000307 700__ $$aKonstantin Beznosov
000000307 8560_ $$fboshmaf@ece.ubc.ca
000000307 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/307/files/paper.pdf
000000307 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/307/files/paper.pdf?subformat=pdfa$$xpdfa
000000307 909C4 $$pYazan Boshmaf, Matei Ripeanu, Konstantin Beznosov. Thwarting Fake OSN Accounts by Predicting their Victims. In Proceedings of the 2015 Workshop on Artificial Intelligent and Security Workshop (AISec'15), Denver, Colorado, USA, Oct, 2015
000000307 980__ $$aRefConfPaper