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Banka Ödemelerinde Dolandırıcılığın Çizge Madenciliği ve Makine Öğrenimi Algoritmalarıyla Tespiti

Year 2021, Volume: 12 Issue: 4, 615 - 625, 29.09.2021
https://doi.org/10.24012/dumf.1002110

Abstract

Günümüzde, şirketler gelecekte yapmayı planladıkları işleri içeren çok sayıdaki önemli verilerini elektronik ortamlarda saklamaktadırlar. Saldırı durumunda ise hem şirkete hem de bireylere zarar verebilecek finansal bilgiler hedef alınmaktadır. Bu saldırı türlerinden biri de banka ödemelerinde meydana gelen dolandırıcılık saldırılarıdır. Grafik veri bilimi kullanılması, mevcut analitik ve makine öğrenimi ardışık düzenlerini güçlendirerek, var olan dolandırıcılık tespit yöntemlerinin doğruluğunu ve uygulanabilirliğini arttırmaktadır. Bu çalışmada İspanya’daki bir banka ödeme bilgi simülasyonundan oluşturulan BankSim veri kümesi kullanılmıştır. BankSim üzerinde bulunan normal ödemeler ve sahte veriler sınıflandırılarak dolandırıcılık tespiti gerçekleştirilmesi amaçlanmıştır. Sınıflandırma için Python dilinde RandomForest (RF), Support Vector Machine SVM, XGBoost (XGB), K-Nearest Neighbors (k-NN) sınıflandırma algoritmaları kullanılmıştır. Performans değerlendirmeleri için K-katlamalı çapraz doğrulama kullanılmıştır. Çizge madenciliği için Neo4j veritabanı kullanılmış ve Neo4j sorgu dili olarak CypherQL kullanılmıştır. Bu dolandırıcılık tespitinin uygulanması ile daha az hileli işlem ve daha güvenilir bir gelir akışı elde edilmiştir. Çizge madenciliği aşamasında PageRank, Community, degree gibi çizge algoritmaları ile birlikte standart makine öğrenimi yöntemi ile elde edilen sonuçlar optimize edilmiştir. Bu açıdan çizge madenciliği ve makine öğrenimi algoritmalarının birlikte kullanılmasının diğer yöntemlere kıyasla doğruluk oranlarının daha yüksek olduğu ve daha hızlı sürede hesap yapan bir yöntem olduğu ispatlanmıştır.

References

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  • [25] Neo4j, URL: https://neo4j.com/developer/graph-database/, (Erişim zamanı: 2021)
  • [26] Kaggle, URL: https://www.kaggle.com/ntnu-testimon/banksim1, E. Alonso, Axelsson, Stefan. Banksim: A bank payments simulator for fraud detection research Inproceedings, (Erişim zamanı: 2021)
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  • [29] V. N. Vapnik. Introduction: Four periods in the research of the learning problem. In The nature of statistical learning theory (pp. 1-15). Springer, New York, NY, 2000.
  • [30] M. R. Segal. Machine learning benchmarks and random forest regression, 2004.
  • [31] R. Mitchell & E. Frank. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3, e127, 2017.
  • [32] M. Sarkar, & T. Y. Leong. Application of K-nearest neighbors algorithm on breast cancer diagnosis problem. In Proceedings of the AMIA Symposium (p. 759). American Medical Informatics Association, 2000.
  • [33] Neo4j, URL: https://neo4j.com/docs/graph-data-science/current/algorithms/page-rank/, (Erişim zamanı: 2021)
  • [34] Neo4j, URL: https://neo4j.com/docs/graph-algorithms/current/labs-algorithms/degree-centrality/, (Erişim zamanı: 2021).
  • [35] Neo4j, URL: https://neo4j.com/docs/graph-data-science/current/algorithms/community/, (Erişim zamanı: 2021).
Year 2021, Volume: 12 Issue: 4, 615 - 625, 29.09.2021
https://doi.org/10.24012/dumf.1002110

Abstract

References

  • [1] G. Sadowski, & P. Rathle. Fraud detection: Discovering connections with graph databases. White Paper-Neo Technology-Graphs are Everywhere, 13, 2014.
  • [2] K. Julisch. Risk-based payment fraud detection. Research Report, IBM Research, Zurich, (2010).
  • [3] S. Rehman, U. Khan, A. U., S. Fong. Graph mining: A survey of graph mining techniques. In Seventh International Conference on Digital Information Management (ICDIM 2012) (pp. 88-92), IEEE, (2012).
  • [4] D. Koutra, C. Faloutsos. Individual and collective graph mining: principles, algorithms, and applications. Synthesis Lectures on Data Mining and Knowledge Discovery, 9(2), 1-206, (2017).
  • [5] C. Jiang, F. Coenen, M. Zito. A survey of frequent subgraph mining algorithms. The Knowledge Engineering Review, 28(1), 75-105i (2013).
  • [6] S. Suthaharan. Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73, (2014).
  • [7] J. Qiu, Wu, Ding Q., G., Xu, Y., S. Feng.A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 2016(1), 1-16,(2016).
  • [8] E. Kurshan, H. Shen, & H. Yu. Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook. In 2020 Second International Conference on Transdisciplinary AI (TransAI) (pp. 125-130). IEEE, September,2020.
  • [9] D. Cheng, X. Wang, Y. Zhang, & L. Zhang. Graph Neural Network for Fraud Detection via Spatial-temporal Attention. IEEE Transactions on Knowledge and Data Engineering, 2020
  • [10] C. Yang, Z. Liu, D. Zhao, Sun, M., & E. Y. Chang. Network representation learning with rich text information. In IJCAI (Vol. 2015, pp. 2111-2117), July, 2015.
  • [11] M. Xie, H. Yin, H. Wang, F., Xu, W. Chen, & S. Wang. Learning graph-based poi embedding for location-based recommendation. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 15-24), October, 2016.
  • [12] A. Roy, , J. Sun, R. Mahoney, L. Alonzi, S. Adams, & P. Beling. Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS) (pp. 129-134). IEEE, April, 2018.
  • [13] H. M. Vidanelage, T. Tasnavijitvong, , P. Suwimonsatein & P. Meesad. Study on machine learning techniques with conventional tools for payment fraud detection. In 2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 1-5). IEEE, October, 2019.
  • [14] F. Carcillo, Y. A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, & G. Bontempi. Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 2019
  • [15] B. Lebichot, Y. A. Le Borgne, L. He-Guelton, F. Oblé, & G. Bontempi, Deep-learning domain adaptation techniques for credit cards fraud detection. In INNS Big Data and Deep Learning conference (pp. 78-88). Springer, Cham, April, 2019
  • [16] C. Wang, & H. Zhu. Representing Fine-Grained Co-Occurrences for Behavior-Based Fraud Detection in Online Payment Services. IEEE Transactions on Dependable and Secure Computing, 2020.
  • [17] P. Shiguihara-Juárez, & N. Murrugarra-Llerena. A Bayesian Classifier Based on Constraints of Ordering of Variables for Fraud Detection. In 2018 Congreso Internacional de Innovación y Tendencias en Ingeniería (CONIITI) (pp. 1-6). IEEE, October, 2018.
  • [18] E. A. Lopez-Rojas,, S. Axelsson. Banksim: A bank payments simulator for fraud detection research Inproceedings. In 26th EuropeanModeling and Simulation Symposium, EMSS, (2014).
  • [19] S. R. Islam. An efficient technique for mining bad credit accounts from both olap and oltp (Doctoral dissertation, Tennessee Technological University), (2018).
  • [20] S. Even. Graph algorithms. Cambridge University Press, 2011.
  • [21] A. Castelltort. Review of Graph-Powered Machine Learning, Alessandro Negro, Manning Publication, 2020.
  • [22] Packpub, URL: https://hub.packtpub.com/neo4j-most-popular-graph-database/, Varangaonkar, A. Why Neo4j is the most popular Graph database. (Erişim zamanı: 2021)
  • [23] R. Wirth, & J. Hipp. CRISP-DM: Towards a standard process model for data mining. In Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining (Vol. 1). London, UK: Springer-Verlag, April 2000.
  • [24] E. A. Lopes-Rojas, & S. Axelsson. Banksim: A bank Payment Simulation for Fraud Detection Research, 2014.
  • [25] Neo4j, URL: https://neo4j.com/developer/graph-database/, (Erişim zamanı: 2021)
  • [26] Kaggle, URL: https://www.kaggle.com/ntnu-testimon/banksim1, E. Alonso, Axelsson, Stefan. Banksim: A bank payments simulator for fraud detection research Inproceedings, (Erişim zamanı: 2021)
  • [27] D. Roobaert. DirectSVM: A simple support vector machine perceptron. Journal of VLSI signal processing systems for signal, image and video technology, 32(1), 147-156, 2002.
  • [28] D. Roobaert. Pedagogical support vector learning: A pure learning approach to object recognition (Doctoral dissertation, Numerisk analys och datalogi), 2001.
  • [29] V. N. Vapnik. Introduction: Four periods in the research of the learning problem. In The nature of statistical learning theory (pp. 1-15). Springer, New York, NY, 2000.
  • [30] M. R. Segal. Machine learning benchmarks and random forest regression, 2004.
  • [31] R. Mitchell & E. Frank. Accelerating the XGBoost algorithm using GPU computing. PeerJ Computer Science, 3, e127, 2017.
  • [32] M. Sarkar, & T. Y. Leong. Application of K-nearest neighbors algorithm on breast cancer diagnosis problem. In Proceedings of the AMIA Symposium (p. 759). American Medical Informatics Association, 2000.
  • [33] Neo4j, URL: https://neo4j.com/docs/graph-data-science/current/algorithms/page-rank/, (Erişim zamanı: 2021)
  • [34] Neo4j, URL: https://neo4j.com/docs/graph-algorithms/current/labs-algorithms/degree-centrality/, (Erişim zamanı: 2021).
  • [35] Neo4j, URL: https://neo4j.com/docs/graph-data-science/current/algorithms/community/, (Erişim zamanı: 2021).
There are 35 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Hande Çavşi Zaim This is me 0000-0002-9032-5145

Esra Yolaçan 0000-0002-0008-1037

Eyyüp Gülbandılar This is me 0000-0001-5559-5281

Early Pub Date September 29, 2021
Publication Date September 29, 2021
Submission Date August 8, 2021
Published in Issue Year 2021 Volume: 12 Issue: 4

Cite

IEEE H. Çavşi Zaim, E. Yolaçan, and E. Gülbandılar, “Banka Ödemelerinde Dolandırıcılığın Çizge Madenciliği ve Makine Öğrenimi Algoritmalarıyla Tespiti”, DUJE, vol. 12, no. 4, pp. 615–625, 2021, doi: 10.24012/dumf.1002110.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456