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.
| Original language | English |
|---|---|
| Title of host publication | 34th International Conference on Machine Learning, ICML 2017 |
| Publisher | International Machine Learning Society (IMLS) |
| Pages | 2391-2401 |
| Number of pages | 11 |
| Volume | 4 |
| ISBN (Electronic) | 9781510855144 |
| Publication status | Published - 2017 Jan 1 |
| Event | 34th International Conference on Machine Learning, ICML 2017 - Sydney, Australia Duration: 2017 Aug 6 → 2017 Aug 11 |
Other
| Other | 34th International Conference on Machine Learning, ICML 2017 |
|---|---|
| Country | Australia |
| City | Sydney |
| Period | 17/8/6 → 17/8/11 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Human-Computer Interaction
- Software
Cite this
Minimizing trust leaks for robust Sybil detection. / Höner, János; Nakajima, Shinichi; Bauer, Alexander; Muller, Klaus; Görnitz, Nico.
34th International Conference on Machine Learning, ICML 2017. Vol. 4 International Machine Learning Society (IMLS), 2017. p. 2391-2401.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Minimizing trust leaks for robust Sybil detection
AU - Höner, János
AU - Nakajima, Shinichi
AU - Bauer, Alexander
AU - Muller, Klaus
AU - Görnitz, Nico
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85048393227
VL - 4
SP - 2391
EP - 2401
BT - 34th International Conference on Machine Learning, ICML 2017
PB - International Machine Learning Society (IMLS)
ER -