Araştırma Makalesi
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Türkiye Telekomünikasyon Sektöründe Sahtecilik Sorunu için Düzenleyici Öneriler

Yıl 2023, , 365 - 376, 23.11.2023
https://doi.org/10.5824/ajite.2023.04.003.x

Öz

İnsanlık tarihinde süregelen sahtecilik sorunu, teknolojik alanlarda yaşanan önemli gelişmelerle birlikte farklı şekillerde karşımıza çıkmaktadır. Telekomünikasyon sektörü, 2000'li yılların başında mobil teknolojilerin yaygınlaşmasıyla büyük dönüşümler yaşamıştır. Bu süreçte telekomünikasyon sahteciliğinde de önemli artışlar yaşanmış ve ciddi maddi ve itibar kayıplarına neden olmuştur. Dolayısıyla tüm sektörlerde olduğu gibi telekomünikasyon alanında da sahtecilikle mücadele ve önleme çalışmaları büyük önem taşımaktadır. Bu çalışma, sahtecilik konusuna ve özellikle telekomünikasyon sahteciliğine odaklanarak, bu alandaki dünya genelinde yaşanan deneyimleri ve önleme çalışmalarını incelemektedir. Ayrıca, Türkiye'deki telekomünikasyon sahteciliği konusundaki mevcut durumu ve beklentileri anlamak amacıyla iki mobil operatörle yapılan odak grup çalışması da bu çalışmaya dahil edilmiştir. Toplanan bilgiler ve mevcut çalışmaların değerlendirmesi sonucunda, Türkiye telekomünikasyon sektöründe sahteciliği azaltmak ve önlemek için düzenleyici öneriler sunulmaktadır.

Kaynakça

  • Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113. https://doi.org/10.1016/j.jnca.2016.04.007
  • Alraouji, Y., & Bramantoro, A. (2014). International call fraud detection systems and techniques. Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems, 159-166. https://doi.org/10.1145/2668260.2668272
  • Becker, R. A., Volinsky, C., & Wilks, A. R. (2010). Fraud detection in telecommunications: History and lessons learned. Technometrics, 52(1), 20-33. https://www.jstor.org/stable/40586677
  • Beneish, M. D. (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16(3), 271-309. https://doi.org/10.1016/S0278-4254(97)00023-9
  • Bilgi Teknolojileri ve İletişim Kurumu. (2022). Elektronik Haberleşme Sektörü 3 Aylık Veriler Raporu 2022 – 1. Çeyrek. https://www.btk.gov.tr/uploads/pages/pazar-verileri/uc-aylik-pazar-verileri-raporu-2022-1.pdf
  • Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-249. https://www.jstor.org/stable/3182781
  • Brown, S. (2005). White Paper Telecommunication Fraud and Management. Waveroad SecurIT,
  • Communications Fraud Control Association. (2019). Communications Fraud Control Association Announces Results of 2019 Global Telecom Fraud Survey. https://cfca.org/document/cfca-2019-fraud-loss-survey-pdf/
  • Cortesão, L., Martins , F., Rosa , A., & Carvalho , P. (n.d.). Fraud Management Systems in Telecommunications: A practical approach. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.553.9706&rep=rep1&type=pdf
  • Electronic Communications Services. (2018). The role of E.164 numbers in international fraud and misuse of electronic communications services. 2018. https://docdb.cept.org/download/1322
  • Electronic Communications Services. (2022). CLI Spoofing. https://nkom.no/telefoni-og-telefonnummer/telefonsvindel/_/attachment/download/34a76b0e-011b-4bc0-b060-cc7761c0ffef:7b521945f6d348bd22fd3569e53458c8347fa51d/ECC%20Report%20338.pdf
  • Farvaresh, H., & Sepehri, M. M. (2011). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence, 24(1), 182-194. https://doi.org/10.1016/j.engappai.2010.05.009
  • GLF. (2018).  Taking action against fraud.
  • Gosset, P., & Hyland, M. (1999). Classification, Detection and Prosecution of Fraud on Mobile Networks. http://www.chrismitchell.net/ASPeCT/CD%20Data/Papers/P31.PDF
  • Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing : A Journal of Practice & Theory, 16(1), 14-28.
  • GSMA. (2021). AB handshake global solution for call validation. https://www.gsma.com/get-involved/gsma-membership/wp-content/uploads/2021/04/AB-Handshake_whitepaper.pdf
  • International Telecommunication Union. (2019). Technical Specification FG DLT D1.1: Distributed ledger technology terms and definitions. https://www.itu.int/en/ITU-T/focusgroups/dlt/Documents/d11.pdf
  • i3Forum. (2019). Calling line ıdentification (clı) management (release 1.0) june 2019. https://i3forum.org/blog/2019/06/18/calling-line-identification-cli/
  • BEREC. (2019). BEREC summary report on the Workshop on Fraud & Misuse of the E . 164 number range. https://www.berec.europa.eu/sites/default/files/files/document_register_store/2019/12/BoR_%2819%29_241_-_Report_Fraud_Misuse_of_Numbers.pdf
  • i3Forum. (2020). I3forum “calling line identification (Cli) spoofing” report. https://i3forum.org/blog/2020/11/04/i3forum-calling-line-identification-cli-spoofing-report/
  • Kuşaksızoğlu, B.  (2006). Fraud detection in mobile communication networks using data mining [Unpublished Master's Thesis]. The University of Bahçeşehir.
  • Mohd Yusoff, M. I., Mohamed, I., & Abu Bakar, M. R. (2013). Improved expectation maximization algorithm for gaussian mixed model using the kernel method. Mathematical Problems in Engineering, 2013, 1-9. https://doi.org/10.1155/2013/757240
  • Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. https://doi.org/10.48550/ARXIV.1009.6119
  • Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. https://doi.org/10.1016/j.dss.2020.113303
  • Rebahi, Y., Thanh, T. Q., Busse, R., & Lorenz, P. (2014). On the use of unsupervised techniques for fraud detection in voip networks. In Emerging Trends in ICT Security (ss. 359-373). Elsevier. https://doi.org/10.1016/B978-0-12-411474-6.00022-0
  • Sahin, M. (2017). Understanding Telephony Fraud as an Essential Step to Better Fight it [Thesis]. École Doctorale Informatique, Télécommunication et Électronique, Paris.
  • Sambra, A.V., Mansour, E., Hawke, S., Zereba, M., Greco, N., Ghanem, A., Zagidulin, D., Aboulnaga, A., & Berners-Lee, T. (2016). Solid :a platform for decentralized social applications based on linked data. http://emansour.com/research/lusail/solid_protocols.pdf
  • Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. The Accounting Review, 73(1), 131-146. https://www.jstor.org/stable/248345
  • Weiss, G. M. (2005). Data mining in telecommunications. In O. Maimon & L. Rokach (Ed.), Data Mining and Knowledge Discovery Handbook (ss. 1189-1201). Springer-Verlag. https://doi.org/10.1007/0-387-25465-X_56
  • Yaga, D., Mell, P., Roby, N., & Scarfone, K. (2018). Blockchain technology overview (NIST IR 8202; s. NIST IR 8202). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8202
  • Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology? —A systematic review. PLOS ONE, 11(10), e0163477. https://doi.org/10.1371/journal.pone.0163477
  • Yufeng Kou, Chang-Tien Lu, Sirwongwattana, S., & Yo-Ping Huang. (2004). Survey of fraud detection techniques. IEEE International Conference on Networking, Sensing and Control, 2004, 2, 749-754. https://doi.org/10.1109/ICNSC.2004.1297040

Regulatory Recommendations for Fraud Problem in The Turkish Telecommunication Sector

Yıl 2023, , 365 - 376, 23.11.2023
https://doi.org/10.5824/ajite.2023.04.003.x

Öz

Fraud has been a persistent issue throughout human history. As technology continues to advance in various fields, fraudulent activities adapt and evolve accordingly. The telecommunication industry, in particular, has undergone significant transformations since the early 2000s with the advent of mobile technologies. It is evident that telecommunication fraud has seen a substantial increase during this time, leading to serious financial and reputational damage. Therefore, combating and preventing fraud has become a crucial task in the telecommunication sector, as it is in all industries. This study delves into the topic of fraud, with a particular emphasis on telecommunication fraud. It investigates the experiences and efforts made to minimize and prevent fraud globally. Additionally, the study includes a focus group analysis involving two mobile operators in Turkey, aiming to understand the current situation and industry expectations concerning telecommunication fraud within the country. After evaluating the information gathered and examining the existing efforts, the study offers a series of regulatory recommendations for reducing and preventing fraud in the Turkish telecommunication sector.

Kaynakça

  • Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of Network and Computer Applications, 68, 90-113. https://doi.org/10.1016/j.jnca.2016.04.007
  • Alraouji, Y., & Bramantoro, A. (2014). International call fraud detection systems and techniques. Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems, 159-166. https://doi.org/10.1145/2668260.2668272
  • Becker, R. A., Volinsky, C., & Wilks, A. R. (2010). Fraud detection in telecommunications: History and lessons learned. Technometrics, 52(1), 20-33. https://www.jstor.org/stable/40586677
  • Beneish, M. D. (1997). Detecting GAAP violation: Implications for assessing earnings management among firms with extreme financial performance. Journal of Accounting and Public Policy, 16(3), 271-309. https://doi.org/10.1016/S0278-4254(97)00023-9
  • Bilgi Teknolojileri ve İletişim Kurumu. (2022). Elektronik Haberleşme Sektörü 3 Aylık Veriler Raporu 2022 – 1. Çeyrek. https://www.btk.gov.tr/uploads/pages/pazar-verileri/uc-aylik-pazar-verileri-raporu-2022-1.pdf
  • Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection: A review. Statistical Science, 17(3), 235-249. https://www.jstor.org/stable/3182781
  • Brown, S. (2005). White Paper Telecommunication Fraud and Management. Waveroad SecurIT,
  • Communications Fraud Control Association. (2019). Communications Fraud Control Association Announces Results of 2019 Global Telecom Fraud Survey. https://cfca.org/document/cfca-2019-fraud-loss-survey-pdf/
  • Cortesão, L., Martins , F., Rosa , A., & Carvalho , P. (n.d.). Fraud Management Systems in Telecommunications: A practical approach. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.553.9706&rep=rep1&type=pdf
  • Electronic Communications Services. (2018). The role of E.164 numbers in international fraud and misuse of electronic communications services. 2018. https://docdb.cept.org/download/1322
  • Electronic Communications Services. (2022). CLI Spoofing. https://nkom.no/telefoni-og-telefonnummer/telefonsvindel/_/attachment/download/34a76b0e-011b-4bc0-b060-cc7761c0ffef:7b521945f6d348bd22fd3569e53458c8347fa51d/ECC%20Report%20338.pdf
  • Farvaresh, H., & Sepehri, M. M. (2011). A data mining framework for detecting subscription fraud in telecommunication. Engineering Applications of Artificial Intelligence, 24(1), 182-194. https://doi.org/10.1016/j.engappai.2010.05.009
  • GLF. (2018).  Taking action against fraud.
  • Gosset, P., & Hyland, M. (1999). Classification, Detection and Prosecution of Fraud on Mobile Networks. http://www.chrismitchell.net/ASPeCT/CD%20Data/Papers/P31.PDF
  • Green, B. P., & Choi, J. H. (1997). Assessing the risk of management fraud through neural network technology. Auditing : A Journal of Practice & Theory, 16(1), 14-28.
  • GSMA. (2021). AB handshake global solution for call validation. https://www.gsma.com/get-involved/gsma-membership/wp-content/uploads/2021/04/AB-Handshake_whitepaper.pdf
  • International Telecommunication Union. (2019). Technical Specification FG DLT D1.1: Distributed ledger technology terms and definitions. https://www.itu.int/en/ITU-T/focusgroups/dlt/Documents/d11.pdf
  • i3Forum. (2019). Calling line ıdentification (clı) management (release 1.0) june 2019. https://i3forum.org/blog/2019/06/18/calling-line-identification-cli/
  • BEREC. (2019). BEREC summary report on the Workshop on Fraud & Misuse of the E . 164 number range. https://www.berec.europa.eu/sites/default/files/files/document_register_store/2019/12/BoR_%2819%29_241_-_Report_Fraud_Misuse_of_Numbers.pdf
  • i3Forum. (2020). I3forum “calling line identification (Cli) spoofing” report. https://i3forum.org/blog/2020/11/04/i3forum-calling-line-identification-cli-spoofing-report/
  • Kuşaksızoğlu, B.  (2006). Fraud detection in mobile communication networks using data mining [Unpublished Master's Thesis]. The University of Bahçeşehir.
  • Mohd Yusoff, M. I., Mohamed, I., & Abu Bakar, M. R. (2013). Improved expectation maximization algorithm for gaussian mixed model using the kernel method. Mathematical Problems in Engineering, 2013, 1-9. https://doi.org/10.1155/2013/757240
  • Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. https://doi.org/10.48550/ARXIV.1009.6119
  • Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, 113303. https://doi.org/10.1016/j.dss.2020.113303
  • Rebahi, Y., Thanh, T. Q., Busse, R., & Lorenz, P. (2014). On the use of unsupervised techniques for fraud detection in voip networks. In Emerging Trends in ICT Security (ss. 359-373). Elsevier. https://doi.org/10.1016/B978-0-12-411474-6.00022-0
  • Sahin, M. (2017). Understanding Telephony Fraud as an Essential Step to Better Fight it [Thesis]. École Doctorale Informatique, Télécommunication et Électronique, Paris.
  • Sambra, A.V., Mansour, E., Hawke, S., Zereba, M., Greco, N., Ghanem, A., Zagidulin, D., Aboulnaga, A., & Berners-Lee, T. (2016). Solid :a platform for decentralized social applications based on linked data. http://emansour.com/research/lusail/solid_protocols.pdf
  • Summers, S. L., & Sweeney, J. T. (1998). Fraudulently misstated financial statements and insider trading: An empirical analysis. The Accounting Review, 73(1), 131-146. https://www.jstor.org/stable/248345
  • Weiss, G. M. (2005). Data mining in telecommunications. In O. Maimon & L. Rokach (Ed.), Data Mining and Knowledge Discovery Handbook (ss. 1189-1201). Springer-Verlag. https://doi.org/10.1007/0-387-25465-X_56
  • Yaga, D., Mell, P., Roby, N., & Scarfone, K. (2018). Blockchain technology overview (NIST IR 8202; s. NIST IR 8202). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.IR.8202
  • Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016). Where is current research on blockchain technology? —A systematic review. PLOS ONE, 11(10), e0163477. https://doi.org/10.1371/journal.pone.0163477
  • Yufeng Kou, Chang-Tien Lu, Sirwongwattana, S., & Yo-Ping Huang. (2004). Survey of fraud detection techniques. IEEE International Conference on Networking, Sensing and Control, 2004, 2, 749-754. https://doi.org/10.1109/ICNSC.2004.1297040
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Siber Güvenlik ve Gizlilik (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Süleyman Bayram 0000-0002-0969-3718

Esma Ergüner Özkoç 0000-0003-1728-5930

Yayımlanma Tarihi 23 Kasım 2023
Gönderilme Tarihi 9 Mayıs 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Bayram, S., & Ergüner Özkoç, E. (2023). Regulatory Recommendations for Fraud Problem in The Turkish Telecommunication Sector. AJIT-E: Academic Journal of Information Technology, 14(55), 365-376. https://doi.org/10.5824/ajite.2023.04.003.x