research-article

Temporal burstiness and collaborative camouflage aware fraud detection

Authors:

Claudio J. Tessone,

Hao LiaoAuthors Info & Claims

Volume 60, Issue 2

https://doi.org/10.1016/j.ipm.2022.103170

Published: 01 March 2023 Publication History

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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Cited By

Xu FWang NWu HWen XZhao XWan HWooldridge MDy JNatarajan S(2024)Revisiting graph-based fraud detection in sight of heterophily and spectrumProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i8.28773(9214-9222)Online publication date: 20-Feb-2024
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Temporal burstiness and collaborative camouflage aware fraud detection

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cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 60, Issue 2

Mar 2023

1443 pages

ISSN:0306-4573

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

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Published: 01 March 2023

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Cited By

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https://dl.acm.org/doi/10.1609/aaai.v38i8.28773
Xylogiannopoulos KXanthopoulos PKarampelas PBakamitsos G(2024)ChatGPT paraphrased product reviews can confuse consumers and undermine their trust in genuine reviews. Can you tell the difference?Information Processing and Management: an International Journal10.1016/j.ipm.2024.10384261:6Online publication date: 1-Nov-2024
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Vorobyev I(2024)Fraud risk assessment in car insurance using claims graph features in machine learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124109251:COnline publication date: 24-Jul-2024
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