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.

Grafos ML is Online!

Telefonica has just open-sourced Grafos ML, a system and tools for large-scale machine learning and graph analytics on top of Apache Giraph. The two main components are Okapi ML Library (machine learning algorithms, which include our recent work on detecting fake accounts in online social services) and RT-Giraph (incremental processing on top of Giraph). This is still an active project that is under heavy development at Telefonica Research, Barcelona.