short-paper

Mining the Networks of Telecommunication Fraud Groups using Social Network Analysis

Authors:

Seng-Cho T. Chou,

Ming-Syan ChenAuthors Info & Claims

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 1128 - 1131

https://doi.org/10.1145/3110025.3119396

Published: 31 July 2017 Publication History

Abstract

Telecommunication fraud is one of the most prevalent crimes nowadays, and causes most property loss of victims. The criminals of telecommunication fraud are highly organized, concealed and transnational, making investigators difficult to track and capture the suspects. In this paper, we propose a Telecom Fraud Analysis Model (TFAM) which can unveil the underlying structure of fraud groups and identify the roles of the fraudsters. The links between suspects are built using flight information, and co-offending records. Social network analysis techniques are applied to analyze group structures as well as influences of each member. We collect a real telecom fraud dataset with 113 fraudsters whose fraudulent activities spread across four countries and 17 cities. Experimental results demonstrate that our method can successfully identify the key roles and discover the hidden structure of the fraud groups.

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Cited By

He CZheng YFei XLi HHu ZTang Y(2022)Boosting Nonnegative Matrix Factorization Based Community Detection With Graph Attention Auto-EncoderIEEE Transactions on Big Data10.1109/TBDATA.2021.31032138:4(968-981)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2021.3103213
Antonio YIndratno SSimanjuntak R(2021)Cyber Insurance Ratemaking: A Graph Mining ApproachRisks10.3390/risks91202249:12(224)Online publication date: 6-Dec-2021
https://doi.org/10.3390/risks9120224
Chouiekh AIbn El Haj E(2021)Towards Spark-Based Deep Learning Approach for Fraud Detection AnalysisProceedings of Sixth International Congress on Information and Communication Technology10.1007/978-981-16-1781-2_2(15-22)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-1781-2_2
Show More Cited By

Mining the Networks of Telecommunication Fraud Groups using Social Network Analysis
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 31 July 2017

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ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

He CZheng YFei XLi HHu ZTang Y(2022)Boosting Nonnegative Matrix Factorization Based Community Detection With Graph Attention Auto-EncoderIEEE Transactions on Big Data10.1109/TBDATA.2021.31032138:4(968-981)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2021.3103213
Antonio YIndratno SSimanjuntak R(2021)Cyber Insurance Ratemaking: A Graph Mining ApproachRisks10.3390/risks91202249:12(224)Online publication date: 6-Dec-2021
https://doi.org/10.3390/risks9120224
Chouiekh AIbn El Haj E(2021)Towards Spark-Based Deep Learning Approach for Fraud Detection AnalysisProceedings of Sixth International Congress on Information and Communication Technology10.1007/978-981-16-1781-2_2(15-22)Online publication date: 10-Sep-2021
https://doi.org/10.1007/978-981-16-1781-2_2
Lin HLiu GWu JZuo YWan XLi H(2020)Fraud Detection in Dynamic Interaction NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.291281732:10(1936-1950)Online publication date: 1-Oct-2020
https://doi.org/10.1109/TKDE.2019.2912817
Chouiekh AEL Haj E(2018)ConvNets for Fraud Detection analysisProcedia Computer Science10.1016/j.procs.2018.01.107127:C(133-138)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1016/j.procs.2018.01.107

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