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Minimizing Trust Leaks for Robust Sybil Detection
János Höner · Shinichi Nakajima · Alexander Bauer · Klaus-robert Mueller · Nico Görnitz

Wed Aug 09 01:30 AM -- 05:00 AM (PDT) @ Gallery #59

Sybil detection is a crucial task to protect online social networks (OSNs) against intruders who try to manipulate automatic services provided by OSNs to their customers. In this paper, we first discuss the robustness of graph-based Sybil detectors SybilRank and Integro and refine theoretically their security guarantees towards more realistic assumptions. After that, we formally introduce adversarial settings for the graph-based Sybil detection problem and derive a corresponding optimal attacking strategy by exploitation of trust leaks. Based on our analysis, we propose transductive Sybil ranking (TSR), a robust extension to SybilRank and Integro that directly minimizes trust leaks. Our empirical evaluation shows significant advantages of TSR over state-of-the-art competitors on a variety of attacking scenarios on artificially generated data and real-world datasets.

Author Information

János Höner (TU Berlin / MathPlan)
Shinichi Nakajima (TU Berlin)
Alexander Bauer (TU Berlin)
Klaus-robert Mueller (Technische Universität Berlin)
Nico Görnitz (TU Berlin)

After an internship with the eScience Group, led by David Heckerman (Microsoft Research, Los Angeles, US) in 2014, Nico received a scholarship and is currently enrolled as a research associate in the machine learning group at the Berlin Institute of Technology (TU Berlin, Berlin, Germany) headed by Klaus-Robert Müller. Before, Nico was employed as a research associate from 2010-2014 and during 2010-2012 also affiliated with the Friedrich Miescher Laboratory of the Max Planck Society in Tübingen, where he was co-advised by Gunnar Rätsch. He received a diploma degree (MSc equivalent) in computer engineering (Technische Informatik) from the Berlin Institute of Technology with a thesis in machine learning for computer security in 2010.

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