Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network
DOI:
https://doi.org/10.35877/454RI.jinav274Keywords:
Fraud Detection, Deep Learning, Modular Neural Network, Credit card Fraud, multi-agent modeling, transaction rulesAbstract
Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus on personal gains and rewards. Study proposes a spectral-clustering hybrid of genetic algorithm trained modular neural network to detect fraud in credit card transactions. The hybrid ensemble seeks to equip credit-card users with a system and algorithm whose knowledge will altruistically detect fraud on credit cards. Results show that the hybrid model effectively differentiates between benign and genuine credit card transactions with a model accuracy of 74%.
References
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[34] R. Feizi., B. Voosoghi., M.R. Ghaffari Razin.. Evaluation of the Efficiency of Adaptive Neuro Fuzzy Inference System in modeling of the Ionosphere Total Electron Content Time Series Case Study: Tehran Permanent GPS Station, Journal of Geomatics Science and Tech., Vol. 8, no.4, Pp. 109-119, 2019
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[36] A.A. Ojugo., E. Ben-Iwhiwhu, O.D. Kekeje., M. Yerokun., I. Iyawah., Malware propagation on time varying networks: comparative study, Int. J. Modern Edu. Comp. Sci., Vol. 6, No. 8, pp. 25-33, doi: 10.5815/ijmecs.2014.08.04, 2014
[37] A.A. Ojugo, D.O. Otakore., Improved early detection of gestational diabetes via intelligent classification models: a case of Niger Delta, J. of Computer Sci. & Application, Vol. 6, No. 2, pp. 82-90, doi: 10.12691/jcsa-6-2-5, 2018
[38] A.A. Ojugo., O. D. Otakore., Forging optimized Bayesian network model with selected parameter for detection of Coronavirus in Delta State Nigeria, J. App. Sci. Eng. Tech. Edu., 3(1): pp37–45, 2021, doi: 10.35877/454RI.asci2163
[39] A.A. Ojugo., A.O. Eboka., Comparative evaluation of high intelligent performance adaptive model for spam phishing detection, Digital Technologies, 3(1): pp9–15, 2018b, [web]: www.sciepub.com/dt/content/3/1
[40] A.P. Aleksey., A.P. Alexander., Kohonen Self-Organizing Map application to representative sample formation in training of multilayer perceptron [web]:researchgate.net/publication/303635615_Kohonen_selforganizing_map_application_to_representative_sample_formation_in_the_training_of_the_multilayer_perceptron, 2016
[41] C. Phua, D. Alahakoon, V. Lee, Minority Report in fraud detection: classification of skewed data, ACM SIGKDD Explorations Newsletter, 6(1), pp. 50-59, 2004
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[43] S. J. Stolfo, D.W. Fan, W. Lee, L.K. Prodromidis, P.K. Chan., Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results, Proc. AAAI Workshop AI Methods in Fraud and Risk Management, pp. 83-90, 1997
[44] S.J. Stolfo, A.L. Prodromidis, Agent-Based Distributed Learning Applied to Fraud Detection, Technical Report CUCS-014-99, Columbia Univ., 1999
[45] Stolfo, S. J., Fan, D. W., Lee, W., Prodromidis, A. L. and Chan, P. K. (2000) “Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project,” Proc. DARPA Information Survivability Conf. and Exposition, vol. 2, pp. 130-144.
[46] Syeda, M., Zhang, Y. Q. and Pan, Y. (2002) “Parallel Granular Networks for Fast Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. Fuzzy Systems, pp. 572-577.
[47] Vatsa, V., Sural, S. and Majumdar, A. K. (2005) “A Game-theoretic Approach to Credit Card Fraud Detection,” Proc. First Int’l Conf. Information Systems Security, pp. 263-276.
[48] Wheeler, R. & Aitken, S. (n.d.) Multiple Algorithms for Fraud Detection Artificial intelligence Applications Institute, The University of Edinburg, Scotland, pp. 1-12, Available from: http://home.cc.gatech.edu/ccl/uploads/45/multiple-algorithms-for-fraud.pdf
[49] Xu, J., Sung, A. H. & Liu, Q. (2007) Behaviour Mining for Fraud Detection Journal of Research and Practice in Information Technology. 39(1), pp. 3–18. Feb. 2007
[2] R.J. Bolton, D.J. Hand, Statistical fraud detection: a review, Statistical Science, 17(3), pp235-255, 2002
[3] A.A. Ojugo., A.O. Eboka., Empirical evaluation on comparative study of machine learning techniques in detection of DDoS, J. Applied Sci. Eng. Tech. & Edu., Vol. 2, No. 1, pp18–27, 2020, doi: 10.35877/454RI.asci2192
[4] A.A. Ojugo, A.O. Eboka., R.E. Yoro., M.O. Yerokun., F.N. Efozia., Framework design for statistical fraud detection, Mathematics and Computers in Sciences and Engineering Series, 50: 176-182, 2015, ISBN: 976-1-61804-327-6.
[5] A.A. Ojugo., D. Allenotor., D.A. Oyemade., O. Longe., C.N. Anujeonye., Comparative stochastic study for credit-card fraud detection models, African J. of Computing and ICT., 8(1-2): pp15 –24, 2015.
[6] A.A. Ojugo., E. Ekurume., Towards a more satisfied user framework through a dependable secured hybrid deep learning ensemble for detection of credit-card fraud, Submitted to WARSE Int. J. of Advanced Trends in Computer Science and Engineering, 2020
[7] L. Delamaire, H. Abdou, Credit card fraud and detection techniques: a review, Banks and Bank Systems, 4(2), pp57, 2009
[8] V. Dheepa, R. Dhanapal, Analysis of Credit Card Fraud Detection Methods, Int. Journal of Recent Trends in Engineering, 2(3), pp126, 2009.
[9] S.J. Stolfo, D.W. Fan, W. Lee, A.L. Prodromidis, Credit card fraud detection using meta learning: issues and initial results, 2015, [online]: http://www.researchgate.net/publication/2282588
[10] A. Marane, Utilizing Visual Analysis for Fraud Detection, Understanding Link Analysis, 2011., [web]: linkanalysisnow.com/2011/09/leveraging-visual-analytics-for.html
[11] I.P. Okobah., A.A. Ojugo., Evolutionary memetic models for malware intrusion detection: a comparative quest for computational solution and convergence, IJCAOnline Int. J. Comp. Application. Vol.179, No. 39. pp34–43, 2018
[12] E.M. Duman, H. Ozcelik, Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications, 38: pp13057–13063, 2011
[13] T. Fawcett, AI Approaches to Fraud Detection and Risk Management, AAAI Workshop. Technical Report WS-97-07, 1997. AAAI Press.
[14] S. Ghosh, D.L. Reilly, Credit Card Fraud Detection with a Neural-Network, Proc. 27th Int. Conf. System Sciences: Information Systems: Decision Support and Knowledge-Based Systems, vol. 3, pp. 621-630, 1994
[15] D.J. Hand, G. Blunt, M.G. Kelly, N.M. Adams., Data mining for fun and profit, Statistical Science, 15(2), pp. 111-131, 2000
[16] E.W. Khin, Employing Artificial Intelligence to Minimize Internet Fraud. Int. J. Cyber Society and Education, 2(1), pp.61-72, 2019, [web]: http://www.academic-journals.org/ojs2/index.php/IJCSE/article/viewFile/753/17
[17] M.J. Kim, T.S. Kim, A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection,” Proc. Int’l Conf. Intelligent Data Eng. and Automated Learning, pp. 378-383, 2002
[18] S. Maes, K. Tuyls, B. Vanschoenwinkel, B. Manderick, Credit Card Fraud Detection, Vrije Universiteit Brussel – Department of Computer Sci., Pleinlaan 2, B-1050, Belgium. [web]:personeel.unimaas.nl/k-tuyls/publications/papers/maenf02.pdf
[19] W.M. Malek, K. Mayes, K. Markantonakis, Fraud Detection and Prevention in Smart Card Based Environments Using Artificial Intelligence. Int. Conf. CARDIS 2008, London, UK, September 8-11, 2008.
[20] A.A. Ojugo., A.O. Eboka., Signature-based malware detection using approximate Boyer Moore string matching algorithm, Int. J. of Math. Sciences & Computing, 3(5): pp49-62, doi: 10.5815/ijmsc.2019.03.05, 2019
[21] A.A. Ojugo, A.O. Eboka., Memetic algorithm for short messaging service spam filter text normalization and semantic approach, Int. J. of Info. & Comm. Tech., Vol. 9, No. 1, pp. 13 – 27, doi: 10.11591/ijict.v9i1.pp9-18, 2020
[22] A.A. Ojugo, A.O. Eboka., Comparative evaluation for high intelligent performance adaptive model for spam phishing detection, Digital Tech., Vol. 3, No.1: pp. 9-15, doi: 10.1269/dt-3-1-1, 2018
[23] S. Tobiyama, Y. Yamaguchi., et al., Malware detection with deep neural network using process behaviour, IEEE 40th Annual Computer Software and Applications Conf., Vol. 2, pp. 577-582, 2016
[24] B. Ghazale., Reasoning Using Modular Neural Network – An Innovative Solution to address question answering AI tasks, retrieved from https://towardsdatascience.com/reasoning-using-modular-neural-networks-f003cb6109a2?gi=7dbcd12eb7c, July 18, 2020
[25] E. Aleskerov, B. Freisleben, B. Rao, CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proc. IEEE Computational Intelligence for Financial Eng., pp. 220-226, 1997
[26] R. Bolton, D. Hand, Unsupervised Profiling Methods for Fraud Detection. Credit Scoring and Credit Control VII, 2001
[27] R. Brause, T. Langsdorf, M. Hepp, Neural Data mining for credit card fraud detection, Proc. IEEE Int. Conf. Tools with Artificial Intelligence, pp. 103-106, 1999.
[28] P. Burge, J. Shawe-Taylor, An Unsupervised Neural, Network Approach to Profiling the Behaviour of Mobile Phone, Users for Use in Fraud Detection. J. Parallel and Distributed Computing 61: 915–925, 2001.
[29] C. Chiu, C. Tsai, A Web Services-Based Collaborative Scheme for credit card fraud detection, Proc. IEEE Int’l Conf. e-Technology, e-Commerce and e-Service, pp. 177-181, 2004
[30] T. Minahan, Fraud detection and prevention. 2013, [web]: nebhe.org/info/pdf/tdbank_breakfast/Fraud_Prevention_and_Detection.pdf
[31] U. Murad, G. Pinkas, G. (1999). Unsupervised Profiling for Identifying Superimposed Fraud. Proc. of PKDD99.
[32] Nielson, A. (2007) “Global Consumer Attitude Towards On-Line Shopping,” http://www2.acnielsen.com/reports/documents/2005_cc_online shopping.pdf ,
[33] Nigrini, M. (2011). Forensic Analytics: Methods and Techniques for Forensic Accounting Investigation. Hoboken, NJ: John Wiley & Sons Inc. ISBN 978-0-470-89046-2. Available from: http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470890460.html
[34] R. Feizi., B. Voosoghi., M.R. Ghaffari Razin.. Evaluation of the Efficiency of Adaptive Neuro Fuzzy Inference System in modeling of the Ionosphere Total Electron Content Time Series Case Study: Tehran Permanent GPS Station, Journal of Geomatics Science and Tech., Vol. 8, no.4, Pp. 109-119, 2019
[35] A.A. Ojugo, A.O. Eboka, Modeling solution of market basket associative rule mining approaches using deep neural net, Digital Tech., 3(1), pp.1–8, doi: 10.12691/dt-3-1-1, 2018
[36] A.A. Ojugo., E. Ben-Iwhiwhu, O.D. Kekeje., M. Yerokun., I. Iyawah., Malware propagation on time varying networks: comparative study, Int. J. Modern Edu. Comp. Sci., Vol. 6, No. 8, pp. 25-33, doi: 10.5815/ijmecs.2014.08.04, 2014
[37] A.A. Ojugo, D.O. Otakore., Improved early detection of gestational diabetes via intelligent classification models: a case of Niger Delta, J. of Computer Sci. & Application, Vol. 6, No. 2, pp. 82-90, doi: 10.12691/jcsa-6-2-5, 2018
[38] A.A. Ojugo., O. D. Otakore., Forging optimized Bayesian network model with selected parameter for detection of Coronavirus in Delta State Nigeria, J. App. Sci. Eng. Tech. Edu., 3(1): pp37–45, 2021, doi: 10.35877/454RI.asci2163
[39] A.A. Ojugo., A.O. Eboka., Comparative evaluation of high intelligent performance adaptive model for spam phishing detection, Digital Technologies, 3(1): pp9–15, 2018b, [web]: www.sciepub.com/dt/content/3/1
[40] A.P. Aleksey., A.P. Alexander., Kohonen Self-Organizing Map application to representative sample formation in training of multilayer perceptron [web]:researchgate.net/publication/303635615_Kohonen_selforganizing_map_application_to_representative_sample_formation_in_the_training_of_the_multilayer_perceptron, 2016
[41] C. Phua, D. Alahakoon, V. Lee, Minority Report in fraud detection: classification of skewed data, ACM SIGKDD Explorations Newsletter, 6(1), pp. 50-59, 2004
[42] C. Phua, V. Lee, K. Smith, R. Gayler, A comprehensive survey of data mining-based fraud detection research, 2007 [web]: www.bsys.monash.edu.au/people/cphua/ .
[43] S. J. Stolfo, D.W. Fan, W. Lee, L.K. Prodromidis, P.K. Chan., Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results, Proc. AAAI Workshop AI Methods in Fraud and Risk Management, pp. 83-90, 1997
[44] S.J. Stolfo, A.L. Prodromidis, Agent-Based Distributed Learning Applied to Fraud Detection, Technical Report CUCS-014-99, Columbia Univ., 1999
[45] Stolfo, S. J., Fan, D. W., Lee, W., Prodromidis, A. L. and Chan, P. K. (2000) “Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project,” Proc. DARPA Information Survivability Conf. and Exposition, vol. 2, pp. 130-144.
[46] Syeda, M., Zhang, Y. Q. and Pan, Y. (2002) “Parallel Granular Networks for Fast Credit Card Fraud Detection,” Proc. IEEE Int’l Conf. Fuzzy Systems, pp. 572-577.
[47] Vatsa, V., Sural, S. and Majumdar, A. K. (2005) “A Game-theoretic Approach to Credit Card Fraud Detection,” Proc. First Int’l Conf. Information Systems Security, pp. 263-276.
[48] Wheeler, R. & Aitken, S. (n.d.) Multiple Algorithms for Fraud Detection Artificial intelligence Applications Institute, The University of Edinburg, Scotland, pp. 1-12, Available from: http://home.cc.gatech.edu/ccl/uploads/45/multiple-algorithms-for-fraud.pdf
[49] Xu, J., Sung, A. H. & Liu, Q. (2007) Behaviour Mining for Fraud Detection Journal of Research and Practice in Information Technology. 39(1), pp. 3–18. Feb. 2007
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Published
2021-01-16
How to Cite
Ojugo, A. A., & Nwankwo, O. (2021). Spectral-Cluster Solution For Credit-Card Fraud Detection Using A Genetic Algorithm Trained Modular Deep Learning Neural Network. JINAV: Journal of Information and Visualization, 2(1), 15–24. https://doi.org/10.35877/454RI.jinav274
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