Security Analysis of Malicious Socialbots on the Web

It’s my pleasure to announce that 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!

Workshop on Social Web Intelligence

It’s my pleasure to announce that I’ll be giving a hands-on tutorial on social web intelligence in February 21st at UBC.

Organized by the SFU/UBC Salon Series on Digital Social Sciences, Humanities and the Arts, the tutorial/workshop will introduce the theory and practice of basic concepts in network analysis, machine learning, and data mining to make sense of the social and information networks that have been fuelled and rendered accessible by the Internet.

Participants will learn about the structure and evolution of networks, drawing on knowledge from disciplines as diverse as sociology, mathematics, statistics, computer science, economics, and physics.

Interactive demonstrations and hands-on analysis of real-world data sets will focus on a range of tasks: from online network data collection, to identifying important nodes in the network, to detecting communities, to opinion mining and sentiment analysis, to predicting future relationships and social attributes.

For event details and RSVP, click here. For the iPython Notebook (slides), click here.

Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs

Our latest research on identifying automated fake accounts in online social networks has been accepted at the 2015 Network and Distributed System Security Symposium (NDSS’15), to be held in Feb in San Diego, USA.

In this work, we present Integro, a scalable defense system that helps OSNs detect fake accounts using a meaningful user ranking scheme. We implemented Integro using Mahout and Giraph in which it scaled nearly linearly. We evaluated Integro against SybilRank, the state-of-the-art in fake account detection, using real-world datasets and a large-scale deployment at Tuenti, the largest OSN in Spain. In particular, we show that Integro significantly outperforms SybilRank in user ranking quality. Moreover, the deployment of Integro at Tuenti resulted in an order of magnitude higher fake account detection precision, as compared to SybilRank.

Integro is published as part of Grafos MLa system and tools for large-scale machine learning and graph analytics on top of Giraph.