Our recent work on fighting against automated fake accounts by predicting their victims has been accepted for publication at the 8th ACM Workshop on Artificial Intelligence and Security (AI-Sec’15), which is collocated with the 22nd ACM Conference on Computer and Communications Security (CCS), Denver, Colorado, USA.
In this work, we start with the observation that 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. We then 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.