Spectrum based fraud detection in social networks

Authors:
Xiaowei Ying;Xintao Wu;Daniel Barbara
Affiliations:
University of North Carolina at Charlotte, USA;University of North Carolina at Charlotte, USA;George Mason University, USA
Venue:
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Year:
2011

Citing 0
Cited 3

A semi-supervised graph-based algorithm for detecting outliers in online-social-networks

Proceedings of the 28th Annual ACM Symposium on Applied Computing
A spectral approach to detecting subtle anomalies in graphs

Journal of Intelligent Information Systems
Graph publication when the protection algorithm is available

Data & Knowledge Engineering

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Abstract

Social networks are vulnerable to various attacks such as spam emails, viral marketing and the such. In this paper we develop a spectrum based detection framework to discover the perpetrators of these attacks. In particular, we focus on Random Link Attacks (RLAs) in which the malicious user creates multiple false identities and interactions among those identities to later proceed to attack the regular members of the network. We show that RLA attackers can be filtered by using their spectral coordinate characteristics, which are hard to hide even after the efforts by the attackers of resembling as much as possible the rest of the network. Experimental results show that our technique is very effective in detecting those attackers and outperforms techniques previously published.