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Detecting fraud in cellular telephone networks
Thesis (MSc)--University of Stellenbosch, 2005.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2012
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| _version_ | 1867613951552913408 |
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| access_status_str | Open Access |
| author | Van Heerden, Johan H. |
| author2 | Van Vuuren, J. H. |
| author_browse | Van Heerden, Johan H. Van Vuuren, J. H. |
| author_facet | Van Vuuren, J. H. Van Heerden, Johan H. |
| author_sort | Van Heerden, Johan H. |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MSc)--University of Stellenbosch, 2005. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/50314 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:44:18.274Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2012 |
| publishDateRange | 2012 |
| publishDateSort | 2012 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/50314 Detecting fraud in cellular telephone networks Van Heerden, Johan H. Van Vuuren, J. H. Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Data mining Cellular telephone systems Fraud Cellular telephone services industry -- South Africa Evaluating data mining techniques Fraud detection Dissertations -- Applied mathematics Theses -- Applied mathematics Thesis (MSc)--University of Stellenbosch, 2005. ENGLISH ABSTRACT: Cellular network operators globally loose between 3% and 5% of their annual revenue to telecommunications fraud. Hence it is of great importance that fraud management systems are implemented to detect, alarm, and shut down fraud within minutes, minimising revenue loss. Modern proprietary fraud management systems employ (i) classification methods, most often artificial neural networks learning from classified call data records to classify new call data records as fraudulent or legitimate, (ii) statistical methods building subscriber behaviour profiles based on the subscriber’s usage in the cellular network and detecting sudden changes in behaviour, and (iii) rules and threshold values defined by fraud analysts, utilising their knowledge of valid fraud cases and the false alarm rate as guidance. The purpose of this thesis is to establish a context for and evaluate the performance of well-known data mining techniques that may be incorporated in the fraud detection process. Firstly, a theoretical background of various well-known data mining techniques is provided and a number of seminal articles on fraud detection, which influenced this thesis, are summarised. The cellular telecommunications industry is introduced, including a brief discussion of the types of fraud experienced by South African cellular network operators. Secondly, the data collection process and the characteristics of the collected data are discussed. Different data mining techniques are applied to the collected data, demonstrating how user behaviour profiles may be built and how fraud may be predicted. An appraisal of the performances and appropriateness of the different data mining techniques is given in the context of the fraud detection process. Finally, an indication of further work is provided in the conclusion to this thesis, in the form of a number of recommendations for possible adaptations of the fraud detection methods, and improvements thereof. A combination of data mining techniques that may be used to build a comprehensive fraud detection model is also suggested. AFRIKAANSE OPSOMMING: Sellulêre netwerk operateurs verloor wêreldwyd tussen 3% en 5% van hul jaarlikse inkomste as gevolg van telekommunikasie bedrog. Dit is dus van die uiterse belang dat bedrog bestuurstelsels geïmplimenteer word om bedrog op te spoor, alarms te genereer, en bedrog binne minute te staak om verlies aan inkomste tot ’n minimum te beperk. Moderne gepatenteerde bedrog bestuurstelsels maak gebruik van (i) klassifikasie metodes, mees dikwels kunsmatige neurale netwerke wat leer vanaf geklassifiseerde oproep rekords en gebruik word om nuwe oproep rekords as bedrog-draend of nie bedrog-draend te klassifiseer, (ii) statistiese metodes wat gedragsprofiele van ’n intekenaar bou, gebaseer op die intekenaar se gedrag in die sellulêre netwerk, en skielike verandering in gedrag opspoor, en (iii) reëls en drempelwaardes wat deur bedrog analiste daar gestel word, deur gebruik te maak van hulle ondervinding met geldige gevalle van bedrog en die koers waarteen vals alarms gegenereer word. Die doel van hierdie tesis is om ’n konteks te bepaal vir en die werksverrigting te evalueer van bekende data ontginningstegnieke wat in bedrog opsporingstelsels gebruik kan word. Eerstens word ’n teoretiese agtergrond vir ’n aantal bekende data ontginningstegnieke voorsien en ’n aantal gedagteryke artikels wat oor bedrog opsporing handel en wat hierdie tesis beïnvloed het, opgesom. Die sellulêre telekommunikasie industrie word bekend gestel, insluitend ’n kort bespreking oor die tipes bedrog wat deur Suid-Afrikaanse sellulˆere telekommunikasie netwerk operateurs ondervind word. Tweedens word die data versamelingsproses en die eienskappe van die versamelde data bespreek. Verskillende data ontginningstegnieke word vervolgens toegepas op die versamelde data om te demonstreer hoe gedragsprofiele van gebruikers gebou kan word en hoe bedrog voorspel kan word. Die werksverrigting en gepastheid van die verskillende data ontginningstegnieke word bespreek in die konteks van die bedrog opsporingsproses. Laastens word ’n aanduiding van verdere werk in die gevolgtrekking tot hierdie tesis verskaf, en wel in die vorm van ’n aantal aanbevelings oor moontlike aanpassings en verbeterings van die bedrog opsporingsmetodes wat beskou en toegepas is. ’n Omvattende bedrog opsporingsmodel wat gebruik maak van ’n kombinasie van data ontginningstegnieke word ook voorgestel. 2012-08-27T11:33:21Z 2012-08-27T11:33:21Z 2005-12 Thesis http://hdl.handle.net/10019.1/50314 en_ZA Stellenbosch University 116 p. : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Data mining Cellular telephone systems Fraud Cellular telephone services industry -- South Africa Evaluating data mining techniques Fraud detection Dissertations -- Applied mathematics Theses -- Applied mathematics Van Heerden, Johan H. Detecting fraud in cellular telephone networks |
| title | Detecting fraud in cellular telephone networks |
| title_full | Detecting fraud in cellular telephone networks |
| title_fullStr | Detecting fraud in cellular telephone networks |
| title_full_unstemmed | Detecting fraud in cellular telephone networks |
| title_short | Detecting fraud in cellular telephone networks |
| title_sort | detecting fraud in cellular telephone networks |
| topic | Data mining Cellular telephone systems Fraud Cellular telephone services industry -- South Africa Evaluating data mining techniques Fraud detection Dissertations -- Applied mathematics Theses -- Applied mathematics |
| url | http://hdl.handle.net/10019.1/50314 |
| work_keys_str_mv | AT vanheerdenjohanh detectingfraudincellulartelephonenetworks |