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

By Binghui Wang, Le Zhang, Neil Zhenqiang Gong. 2018

29 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Information Systems, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Binghui Wang
2. Neil Zhenqiang Gong
3. Jinyuan Jia
4. Le Zhang
5. Hao Fu

In Information Systems

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Jinyuan Jia
4. Le Zhang
5. Hao Fu

In Sociology and Political Science

1. Binghui Wang
2. Neil Zhenqiang Gong
3. Jinyuan Jia

Citations in the corpus

• Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Graph-based classification methods have been widely applied to various security problems such as malware detection [14, 59], fraudulent user detection in social networks [10, 25, 36, 67, 68, 72, 74], fake review detection [1, 65], auction fraud detection [47], APT infection detection [46], and attri”
• Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, background): “Specifically, Wang et al. [55] recently proposed SybilBlind, which generalizes LBP-based collective classification methods to the scenarios where no training dataset is available.”