Minimizing Trust Leaks for Robust Sybil Detection

János Höner, Shinichi Nakajima, Alexander Bauer, Klaus-Robert Müller, Nico Görnitz
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:1520-1528, 2017.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v70-honer17a, title = {Minimizing Trust Leaks for Robust {S}ybil Detection}, author = {J{\'a}nos H{\"o}ner and Shinichi Nakajima and Alexander Bauer and Klaus-Robert M{\"u}ller and Nico G{\"o}rnitz}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {1520--1528}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/honer17a/honer17a.pdf}, url = {https://proceedings.mlr.press/v70/honer17a.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T Minimizing Trust Leaks for Robust Sybil Detection %A János Höner %A Shinichi Nakajima %A Alexander Bauer %A Klaus-Robert Müller %A Nico Görnitz %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-honer17a %I PMLR %P 1520--1528 %U https://proceedings.mlr.press/v70/honer17a.html %V 70 %X 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.
APA
Höner, J., Nakajima, S., Bauer, A., Müller, K. & Görnitz, N.. (2017). Minimizing Trust Leaks for Robust Sybil Detection. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:1520-1528 Available from https://proceedings.mlr.press/v70/honer17a.html.

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