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Graph-Based Fraud Detection in the Face of Camouflage

Authors:

Christos FaloutsosAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4

Article No.: 44, Pages 1 - 26

https://doi.org/10.1145/3056563

Published: 29 June 2017 Publication History

Abstract

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.

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Yu PTan C(2025)Unraveling the Viral Spread of Misinformation: Maximum-Likelihood Estimation and Starlike Tree Approximation in Markovian Spreading ModelsIEEE Transactions on Signal Processing10.1109/TSP.2025.352775573(446-461)Online publication date: 2025
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Index Terms

Graph-Based Fraud Detection in the Face of Camouflage
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Content ranking

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 4

Special Issue on KDD 2016 and Regular Papers

November 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3119906

Editor:
Jie Tang
Tsinghua University, China

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Copyright © 2017 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 June 2017

Accepted: 01 February 2017

Revised: 01 January 2017

Received: 01 November 2016

Published in TKDD Volume 11, Issue 4

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Cited By

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https://doi.org/10.1109/TSP.2025.3527755
Yu YShao MLi XWang W(2025)Temporal multivariate-factors independence convolution network for anomaly detection in dynamic networksNeurocomputing10.1016/j.neucom.2025.129439(129439)Online publication date: Jan-2025
https://doi.org/10.1016/j.neucom.2025.129439
Chakraborty SSharov S(2025)Graph based approach on financial fraudulent detection and predictionApplied Graph Data Science10.1016/B978-0-443-29654-3.00019-3(25-37)Online publication date: 2025
https://doi.org/10.1016/B978-0-443-29654-3.00019-3
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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Jing SChen LLi QWu D(2024)DOS-GNN: Dual-Feature Aggregations with Over-Sampling for Class-Imbalanced Fraud Detection On Graphs2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650494(1-8)Online publication date: 30-Jun-2024
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