Thwarting Fake OSN Accounts by Predicting their Victims

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.

Security Analysis of Malicious Socialbots on the Web

Good news! I have successfully defended my PhD dissertation titled “Security Analysis of Malicious Socialbots on the Web.” It is available here.

I would like to thank my examination committee members, namely, Konstantin Beznosov (co-advisor), Matei Ripeanu (co-advisor), William (Bill) Aiello, Sidney Fels, and David Lie (external, University of Toronto). I’m also grateful to all my friends and colleagues who have been there for me. Thank you folks!

While it has been a long and humbling journey, I cannot wait to start a new one. I’ll keep you updated!