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Temporal burstiness and collaborative camouflage aware fraud detection


Zhang, Zheng; Wan, Jun; Zhou, Mingyang; Lai, Zhihui; Tessone, Claudio J; Chen, Guoliang; Liao, Hao (2023). Temporal burstiness and collaborative camouflage aware fraud detection. Information Processing & Management, 60(2):103170.

Abstract

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.

Abstract

With the prosperity and development of the digital economy, many fraudsters have emerged on e-commerce platforms to fabricate fraudulent reviews to mislead consumers’ shopping decisions for profit. Moreover, in order to evade fraud detection, fraudsters continue to evolve and present the phenomenon of adversarial camouflage and collaborative attack. In this paper, we propose a novel temporal burstiness and collaborative camouflage aware method (TBCCA) for fraudster detection. Specifically, we capture the hidden temporal burstiness features behind camouflage strategy based on the time series prediction model, and identify highly suspicious target products by assigning suspicious scores as node priors. Meanwhile, a propagation graph integrating review collusion is constructed, and an iterative fraud confidence propagation algorithm is designed for inferring the label of nodes in the graph based on Loop Belief Propagation (LBP). Comprehensive experiments are conducted to compare TBCCA with state-of-the-art fraudster detection approaches, and experimental results show that TBCCA can effectively identify fraudsters in real review networks with achieving 6%–10% performance improvement than other baselines.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
08 Research Priority Programs > Social Networks
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Information Systems
Physical Sciences > Media Technology
Physical Sciences > Computer Science Applications
Social Sciences & Humanities > Management Science and Operations Research
Social Sciences & Humanities > Library and Information Sciences
Uncontrolled Keywords:Fraudster detection, Temporal burstiness, Collaborative camouflage, ARIMA model, Pairwise Markov Random Field
Scope:Discipline-based scholarship (basic research)
Language:English
Date:1 March 2023
Deposited On:16 Feb 2024 11:36
Last Modified:31 May 2024 01:55
Publisher:Elsevier
Number of Pages:103170
ISSN:0306-4573
OA Status:Closed
Publisher DOI:https://doi.org/10.1016/j.ipm.2022.103170