DFraud³: Multi-Component Fraud Detection Free of Cold-Start

Fraud review detection is a hot research topic in recent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approac...

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Bibliographic Details
Published in:IEEE transactions on information forensics and security 2021, Vol.16, p.3456-3468
Main Authors: Shehnepoor, Saeedreza, Togneri, Roberto, Liu, Wei, Bennamoun, Mohammed
Format: Article
Language:English
Subjects:
Australia
Camouflage
Classification
Cold starts
cold-start
Feature extraction
Fraud
inductive learning
Knowledge representation
Linear programming
multi-component classification
Performance gain
Social media
Surges
Training
Writing
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Summary:Fraud review detection is a hot research topic in recent years. The Cold-start is a particularly new but significant problem referring to the failure of a detection system to recognize the authenticity of a new user. State-of-the-art solutions employ a translational knowledge graph embedding approach ( TransE ) to model the interaction of the components of a review system. However, these approaches suffer from the limitation of TransE in handling N-1 relations and the narrow scope of a single classification task, i.e., detecting fraudsters only. In this paper, we model a review system as a Heterogeneous Information Network (HIN) which enables a unique representation to every component and performs graph inductive learning on the review data through aggregating features of nearby nodes. HIN with graph induction helps to address the camouflage issue (fraudsters with genuine reviews) which has shown to be more severe when it is coupled with cold-start, i.e., new fraudsters with genuine first reviews. In this research, instead of focusing only on one component, detecting either fraud reviews or fraud users (fraudsters), vector representations are learned for each component, enabling multi-component classification. In other words, we can detect fraud reviews, fraudsters, and fraud-targeted items, thus the name of our approach DFraud 3 . DFraud 3 demonstrates a significant accuracy increase of 13% over the state of the art on Yelp.
ISSN:1556-6013
1556-6021
DOI:10.1109/TIFS.2021.3081258