tutorial

International Call Fraud Detection Systems and Techniques

Authors:

Yousef Alraouji,

Arif BramantoroAuthors Info & Claims

MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

Pages 159 - 166

https://doi.org/10.1145/2668260.2668272

Published: 15 September 2014 Publication History

Abstract

In recent years, fraud in telecommunication industry becomes one of encumbrance for a telecommunication operator which is growing dramatically. It is befall a serious international problem for GSM and PSTN network service providers. It has undoubtedly become a significant source of revenue losses and bad debts to the telecommunication industry, and with the expected continuing growth in revenue it can be expected that fraud will increase proportionally. It has become a mainreason of revenue losses in the industry of telecommunications. This study focuses on International call fraud detection system and its techniques. It proposes a new technique to detect fraud in international call by classifying the CDRs for roaming subscribers. SIM Boxes (also known as GSM Gateways) causes significant interconnect revenue losses for mobile operators by bypassing official interconnections which makes the operators lose millions of wholesale minutes. This research provides an algorithm to determine suspected fraud number. Even if ordinal CLI is unavailable, the solution can successfully track calls path. The proposed algorithm enables telecommunication operators apply fraud detection solution at minimum cost of operation. There are two main parts of study. The first one is the theory of fraud in telecommunication operators, fraud management system and its techniques. The second one is the implemented solution to detect fraud in international call by utilizing homework data.

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Cited By

Nazmi HSiau NBramantoro ASuhaili W(2023)Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniquesInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2023.07.01310:7(109-126)Online publication date: Jul-2023
https://doi.org/10.21833/ijaas.2023.07.013
Yehya BSalhab N(2023)Telecommunications Fraud Machine Learning-based Detection2023 4th International Conference on Data Analytics for Business and Industry (ICDABI)10.1109/ICDABI60145.2023.10629612(656-661)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICDABI60145.2023.10629612
Rivas MRoach RHosein P(2022)Premium Rate Services Fraud Detection2022 24th International Conference on Advanced Communication Technology (ICACT)10.23919/ICACT53585.2022.9728971(380-386)Online publication date: 13-Feb-2022
https://doi.org/10.23919/ICACT53585.2022.9728971
Show More Cited By

Index Terms

International Call Fraud Detection Systems and Techniques
1. Information systems
  1. Information systems applications

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cover image ACM Other conferences

MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

September 2014

225 pages

ISBN:9781450327671

DOI:10.1145/2668260

Conference Chairs:
Obaid Al Motairy
Qassim University, KSA
,
Richard Chbeir
University of Pau and Adour Countries, France
,
Mohammed Alodib
Qassim University, KSA
,
Lamri Laouamer
Qassim University, KSA
,
Youakim Badr
INSA De Lyon, France
,
Program Chairs:
Morad Benyoucef
University of Ottawa, Canada
,
Saad Harous
United Arab Emirates University
,
Ismail Biskri

Copyright © 2014 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 ACM 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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SIGAPP: ACM Special Interest Group on Applied Computing

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New York, NY, United States

Publication History

Published: 15 September 2014

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MEDES '14

MEDES '14: The 6th International Conference on Management of Emergent Digital EcoSystems

September 15 - 17, 2014

Al Qassim, Buraidah, Saudi Arabia

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Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Nazmi HSiau NBramantoro ASuhaili W(2023)Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniquesInternational Journal of ADVANCED AND APPLIED SCIENCES10.21833/ijaas.2023.07.01310:7(109-126)Online publication date: Jul-2023
https://doi.org/10.21833/ijaas.2023.07.013
Yehya BSalhab N(2023)Telecommunications Fraud Machine Learning-based Detection2023 4th International Conference on Data Analytics for Business and Industry (ICDABI)10.1109/ICDABI60145.2023.10629612(656-661)Online publication date: 25-Oct-2023
https://doi.org/10.1109/ICDABI60145.2023.10629612
Rivas MRoach RHosein P(2022)Premium Rate Services Fraud Detection2022 24th International Conference on Advanced Communication Technology (ICACT)10.23919/ICACT53585.2022.9728971(380-386)Online publication date: 13-Feb-2022
https://doi.org/10.23919/ICACT53585.2022.9728971
Kouam AViana ATchana A(2021) SIMBox Bypass Frauds in Cellular Networks: Strategies, Evolution, Detection, and Future Directions IEEE Communications Surveys & Tutorials10.1109/COMST.2021.310091623:4(2295-2323)Online publication date: Dec-2022
https://doi.org/10.1109/COMST.2021.3100916
Veloso BTabassum SMartins CEspanha RAzevedo RGama J(2020)Interconnect bypass fraud detection: a case studyAnnals of Telecommunications10.1007/s12243-020-00808-wOnline publication date: 19-Sep-2020
https://doi.org/10.1007/s12243-020-00808-w
Subudhi SPanigrahi SBehera T(2016)Detection of Mobile Phone Fraud Using Possibilistic Fuzzy C-Means Clustering and Hidden Markov ModelInternational Journal of Synthetic Emotions10.4018/IJSE.20160701027:2(23-44)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.4018/IJSE.2016070102

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