Thwarting Fake OSN Accounts by Predicting their Victims
Venue
AI-Sec'2015, ACM (to appear)
Publication Year
2015
Authors
Yazan Boshmaf, Matei Ripeanu, Konstantin Beznosov, Elizeu Santos-Neto
BibTeX
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.
