# SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection

arXiv: https://arxiv.org/abs/1312.5035 · HTML: https://mlsanity.com/paper/1312.5035/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2013.

## Built on by (most cited first, 11 of 11)

- [FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information](https://mlsanity.com/paper/2210.10936/) (2022)
- [Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications](https://mlsanity.com/paper/2110.08760/) (2021)
- [Data Poisoning Attacks to Deep Learning Based Recommender Systems](https://mlsanity.com/paper/2101.02644/) (2021)
- [Understanding the Security of Deepfake Detection](https://mlsanity.com/paper/2107.02045/) (2021)
- [Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers](https://mlsanity.com/paper/2308.05353/) (2023)
- [Sybil Detection using Graph Neural Networks](https://mlsanity.com/paper/2409.08631/) (2024)
- [Predictive Response Optimization: Using Reinforcement Learning to Fight Online Social Network Abuse](https://mlsanity.com/paper/2502.17693/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Signed Latent Factors for Spamming Activity Detection](https://mlsanity.com/paper/2209.13814/) (2022)
- [Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs](https://mlsanity.com/paper/2507.16860/) (2025)
- [CleanBase: Detecting Malicious Documents in RAG Knowledge Databases](https://mlsanity.com/paper/2605.00460/) (2026)

## Abstract (arXiv)

Sybil attacks are a fundamental threat to the security of distributed systems. Recently, there has been a growing interest in leveraging social networks to mitigate Sybil attacks. However, the existing approaches suffer from one or more drawbacks, including bootstrapping from either only known benign or known Sybil nodes, failing to tolerate noise in their prior knowledge about known benign or Sybil nodes, and being not scalable. In this work, we aim to overcome these drawbacks. Towards this goal, we introduce SybilBelief, a semi-supervised learning framework, to detect Sybil nodes. SybilBelief takes a social network of the nodes in the system, a small set of known benign nodes, and, optionally, a small set of known Sybils as input. Then SybilBelief propagates the label information from the known benign and/or Sybil nodes to the remaining nodes in the system. We evaluate SybilBelief using both synthetic and real world social network topologies. We show that SybilBelief is able to accurately identify Sybil nodes with low false positive rates and low false negative rates. SybilBelief is resilient to noise in our prior knowledge about known benign and Sybil nodes. Moreover, SybilBelief performs orders of magnitudes better than existing Sybil classification mechanisms and significantly better than existing Sybil ranking mechanisms.