# SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels

arXiv: https://arxiv.org/abs/1806.04853 · HTML: https://mlsanity.com/paper/1806.04853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 1 of 1)

- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)

## Abstract (arXiv)

Detecting fake users (also called Sybils) in online social networks is a basic security research problem. State-of-the-art approaches rely on a large amount of manually labeled users as a training set. These approaches suffer from three key limitations: 1) it is time-consuming and costly to manually label a large training set, 2) they cannot detect new Sybils in a timely fashion, and 3) they are vulnerable to Sybil attacks that leverage information of the training set. In this work, we propose SybilBlind, a structure-based Sybil detection framework that does not rely on a manually labeled training set. SybilBlind works under the same threat model as state-of-the-art structure-based methods. We demonstrate the effectiveness of SybilBlind using 1) a social network with synthetic Sybils and 2) two Twitter datasets with real Sybils. For instance, SybilBlind achieves an AUC of 0.98 on a Twitter dataset.