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Abstract: Traditional 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.
Keyword(s): Fake Accounts ; Online Social Networks ; Applied Machine Learning ; Social Botnets
Published in: Yazan 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:
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Refereed Conference Papers