Graph-Based Fraud Detection in the Face of Camouflage

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Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage, by adding reviews or follows with honest targets so that they look "normal." Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real- world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real- world experiments with a Twitter follower-followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4,000 detected accounts, of which a majority had tweets showing that they used follower-buying services.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2017-08
Language
English
Article Type
Article; Proceedings Paper
Citation

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v.11, no.4

ISSN
1556-4681
DOI
10.1145/3056563
URI
http://hdl.handle.net/10203/250517
Appears in Collection
EE-Journal Papers(저널논문)
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