SybilGuard: defending against sybil attacks via social networks
Open accessAbstract
Peer-to-peer and other decentralized,distributed systems are known to be particularly vulnerable to sybil attacks. In a sybil attack,a malicious user obtains multiple fake identities and pretends to be multiple, distinct nodes in the system. By controlling a large fraction of the nodes in the system,the malicious user is able to "out vote" the honest users in collaborative tasks such as Byzantine failure defenses. This paper presents SybilGuard, a novel protocol for limiting the corruptive influences of sybil attacks.Our protocol is based on the "social network "among user identities, where an edge between two identities indicates a human-established trust relationship. Malicious users can create many identities but few trust relationships. Thus, there is a disproportionately-small "cut" in the graph between the sybil nodes and the honest nodes. SybilGuard exploits this property to bound the number of identities a malicious user can create.We show the effectiveness of SybilGuard both analytically and experimentally.
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Prior work has focused extensively on analysis and detection of sybil and/or fake accounts in online social networks by relying on tightly-knit community structures.
“Prior work has focused quite extensively on the analysis and the detection of sybil and/or fake accounts in online social networks by relying on tightly-knit community structures [54, 15, 53, 9, 52, 5].”
From Measuring, Characterizing, and Detecting Facebook Like Farms (2017) · cites this paper as [54]
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Toward Optimal Community Detection: From Trees to General Weighted Networks
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Security Issues of Online Social Networks
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SOFIA: Social Filtering for Robust Recommendations
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- Authors
- Yu, H., Kaminsky, M., Gibbons, P. B., & Flaxman, A.
- Affiliations
- Intel (United States) (US)
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
- Publisher
- ACM
- Published
- 2006-08-11
- Pages
- 267-278
- OpenAlex
- W4240443285
- Cited by
- 260
- References
- 21
- Citations by year
- 2026: 7 · 2025: 4 · 2024: 10 · 2023: 8 · 2022: 4 · 2021: 9 · 2020: 8 · 2019: 7 · 2018: 14 · 2017: 12 · 2016: 18 · 2015: 16 · 2014: 20 · 2013: 14 · 2012: 21
- FWCI
- 32.14
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGCOMM Conference (SIGCOMM) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Access Control and Trust
- Physical Sciences › Computer Science › Computer Networks and Communications › Peer-to-Peer Network Technologies
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- Sybil attack, peer-to-peer networks, decentralized systems, trust relationships, social networks, graph cuts, Byzantine fault tolerance, identity spoofing, malicious node detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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