research-article

FeatNet: Large-scale Fraud Device Detection by Network Representation Learning with Rich Features

Authors:

Tao WeiAuthors Info & Claims

AISec '18: Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security

Pages 57 - 63

https://doi.org/10.1145/3270101.3270109

Published: 15 January 2018 Publication History

Abstract

Online fraud such as search engine poisoning, groups of fake accounts and opinion fraud is conducted by fraudsters controlling a large number of mobile devices. The key to detect such fraudulent activities is to identify devices controlled by fraudsters. Traditional approaches that fingerprint devices based on device metadata only consider single device information. However, these techniques do not utilize the relationship among different devices, which is crucial to detect fraudulent activities. In this paper, we propose an effective device fraud detection framework called FeatNet, which incorporates device features and device relationships in network representation learning. Specifically, we partition the device network into bipartite graphs and generate the neighborhoods of vertices by revised truncated random walk. Then, we generate the feature signature according to device features to learn the representation of devices. Finally, the embedding vectors of all bipartite graphs are used for fraud detection. We conduct experiments on a large-scale data set and the result shows that our approach can achieve better accuracy than existing algorithms and can be deployed in the real production environment with high performance.

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Cited By

Van Belle RBaesens BDe Weerdt J(2023)CATCHM: A novel network-based credit card fraud detection method using node representation learningDecision Support Systems10.1016/j.dss.2022.113866164(113866)Online publication date: Jan-2023
https://doi.org/10.1016/j.dss.2022.113866
Lam HPho KYoshitaka A(2023)AdVLO: Region Selection via Attention-Driven for Visual LiDAR OdometryIntelligent Information and Database Systems10.1007/978-981-99-5834-4_7(85-96)Online publication date: 5-Sep-2023
https://doi.org/10.1007/978-981-99-5834-4_7
Van Belle RMitrović SDe Weerdt J(2020)Representation Learning in Graphs for Credit Card Fraud DetectionMining Data for Financial Applications10.1007/978-3-030-37720-5_3(32-46)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-37720-5_3

Index Terms

FeatNet: Large-scale Fraud Device Detection by Network Representation Learning with Rich Features
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
    2. Machine learning approaches

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Published In

cover image ACM Conferences

AISec '18: Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security

October 2018

103 pages

ISBN:9781450360043

DOI:10.1145/3270101

Program Chairs:
Sadia Afroz
ICSI, UC Berkeley, USA
,
Battista Biggio
Univ Rennes, CNRS, IRISA, France
,
Yuval Elovici
Ben-Gurion University of the Negev, Israel
,
David Freeman
Facebook, USA
,
Asaf Shabtai
Ben-Gurion University of the Negev, Israel

Copyright © 2018 ACM.

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SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 January 2018

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CCS '18

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SIGSAC

CCS '18: 2018 ACM SIGSAC Conference on Computer and Communications Security

October 15 - 19, 2018

Toronto, Canada

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AISec '18 Paper Acceptance Rate 9 of 32 submissions, 28%;

Overall Acceptance Rate 94 of 231 submissions, 41%

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CCS '24

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ACM SIGSAC Conference on Computer and Communications Security

October 14 - 18, 2024

Salt Lake City , UT , USA

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Cited By

Van Belle RBaesens BDe Weerdt J(2023)CATCHM: A novel network-based credit card fraud detection method using node representation learningDecision Support Systems10.1016/j.dss.2022.113866164(113866)Online publication date: Jan-2023
https://doi.org/10.1016/j.dss.2022.113866
Lam HPho KYoshitaka A(2023)AdVLO: Region Selection via Attention-Driven for Visual LiDAR OdometryIntelligent Information and Database Systems10.1007/978-981-99-5834-4_7(85-96)Online publication date: 5-Sep-2023
https://doi.org/10.1007/978-981-99-5834-4_7
Van Belle RMitrović SDe Weerdt J(2020)Representation Learning in Graphs for Credit Card Fraud DetectionMining Data for Financial Applications10.1007/978-3-030-37720-5_3(32-46)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-37720-5_3

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