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Detecting SIM box fraud by using support vector machine and artificial neural network

Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box...

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Main Authors: Sallehuddin, Roselina, Ibrahim, Subariah, Mohd. Zain, Azlan, Elmi, Abdikarim Hussein
Format: Article
Language:English
Published: Penerbit UTM Press 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/58251/1/RoselinaSallehuddin2015_DetectingSIMBoxFraud.pdf
http://eprints.utm.my/id/eprint/58251/
http://dx.doi.org/10.11113/jt.v74.2649
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spelling my.utm.582512022-04-07T05:46:22Z http://eprints.utm.my/id/eprint/58251/ Detecting SIM box fraud by using support vector machine and artificial neural network Sallehuddin, Roselina Ibrahim, Subariah Mohd. Zain, Azlan Elmi, Abdikarim Hussein QA75 Electronic computers. Computer science Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training. Penerbit UTM Press 2015 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/58251/1/RoselinaSallehuddin2015_DetectingSIMBoxFraud.pdf Sallehuddin, Roselina and Ibrahim, Subariah and Mohd. Zain, Azlan and Elmi, Abdikarim Hussein (2015) Detecting SIM box fraud by using support vector machine and artificial neural network. Jurnal Teknologi, 74 (1). pp. 137-149. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v74.2649 DOI: 10.11113/jt.v74.2649
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Sallehuddin, Roselina
Ibrahim, Subariah
Mohd. Zain, Azlan
Elmi, Abdikarim Hussein
Detecting SIM box fraud by using support vector machine and artificial neural network
description Fraud in communication has been increasing dramatically due to the new modern technologies and the global superhighways of communication, resulting in loss of revenues and quality of service in telecommunication providers especially in Africa and Asia. One of the dominant types of fraud is SIM box bypass fraud whereby SIM cards are used to channel national and multinational calls away from mobile operators and deliver as local calls. Therefore it is important to find techniques that can detect this type of fraud efficiently. In this paper, two classification techniques, Artificial Neural Network (ANN) and Support Vector Machine (SVM) were developed to detect this type of fraud. The classification uses nine selected features of data extracted from Customer Database Record. The performance of ANN is compared with SVM to find which model gives the best performance. From the experiments, it is found that SVM model gives higher accuracy compared to ANN by giving the classification accuracy of 99.06% compared with ANN model, 98.71% accuracy. Besides, better accuracy performance, SVM also requires less computational time compared to ANN since it takes lesser amount of time in model building and training.
format Article
author Sallehuddin, Roselina
Ibrahim, Subariah
Mohd. Zain, Azlan
Elmi, Abdikarim Hussein
author_facet Sallehuddin, Roselina
Ibrahim, Subariah
Mohd. Zain, Azlan
Elmi, Abdikarim Hussein
author_sort Sallehuddin, Roselina
title Detecting SIM box fraud by using support vector machine and artificial neural network
title_short Detecting SIM box fraud by using support vector machine and artificial neural network
title_full Detecting SIM box fraud by using support vector machine and artificial neural network
title_fullStr Detecting SIM box fraud by using support vector machine and artificial neural network
title_full_unstemmed Detecting SIM box fraud by using support vector machine and artificial neural network
title_sort detecting sim box fraud by using support vector machine and artificial neural network
publisher Penerbit UTM Press
publishDate 2015
url http://eprints.utm.my/id/eprint/58251/1/RoselinaSallehuddin2015_DetectingSIMBoxFraud.pdf
http://eprints.utm.my/id/eprint/58251/
http://dx.doi.org/10.11113/jt.v74.2649
_version_ 1729703227720466432
score 13.211869

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