Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study
DOI:
https://doi.org/10.33633/jcta.v1i2.9259Keywords:
HyDelMoNNE, credit-card, fraud detection, deep learning ensemble, reinforcement modelAbstract
The advent of the Internet as an effective means for resource sharing has consequently, led to proliferation of adversaries, with unauthorized access to network resources. Adversaries achieved fraudulent activities via carefully crafted attacks of large magnitude targeted at personal gains and rewards. With the cost of over $1.3Trillion lost globally to financial crimes and the rise in such fraudulent activities vis the use of credit-cards, financial institutions and major stakeholders must begin to explore and exploit better and improved means to secure client data and funds. Banks and financial services must harness the creative mode rendered by machine learning schemes to help effectively manage such fraud attacks and threats. We propose HyGAMoNNE – a hybrid modular genetic algorithm trained neural network ensemble to detect fraud activities. The hybrid, equipped with knowledge to altruistically detect fraud on credit card transactions. Results show that the hybrid effectively differentiates, the benign class attacks/threats from genuine credit card transaction(s) with model accuracy of 92%.References
R. Broadhurst, K. Skinner, N. Sifniotis, and B. Matamoros-Macias, “Cybercrime Risks in a University Student Community,” SSRN Electron. J., no. May, 2018, doi: 10.2139/ssrn.3176319.
R. E. Yoro, F. O. Aghware, B. O. Malasowe, O. Nwankwo, and A. A. Ojugo, “Assessing contributor features to phishing susceptibility amongst students of petroleum resources varsity in Nigeria,” Int. J. Electr. Comput. Eng., vol. 13, no. 2, p. 1922, Apr. 2023, doi: 10.11591/ijece.v13i2.pp1922-1931.
F. O. Aghware, R. E. Yoro, P. O. Ejeh, C. C. Odiakaose, F. U. Emordi, and A. A. Ojugo, “DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 6, pp. 94–100, 2023, doi: 10.14569/IJACSA.2023.0140610.
A. Artikis et al., “A Prototype for Credit Card Fraud Management,” in Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems, Jun. 2017, pp. 249–260. doi: 10.1145/3093742.3093912.
I. Benchaji, S. Douzi, B. El Ouahidi, and J. Jaafari, “Enhanced credit card fraud detection based on attention mechanism and LSTM deep model,” J. Big Data, vol. 8, no. 1, p. 151, Dec. 2021, doi: 10.1186/s40537-021-00541-8.
R. E. Yoro, F. O. Aghware, M. I. Akazue, A. E. Ibor, and A. A. Ojugo, “Evidence of personality traits on phishing attack menace among selected university undergraduates in Nigerian,” Int. J. Electr. Comput. Eng., vol. 13, no. 2, p. 1943, Apr. 2023, doi: 10.11591/ijece.v13i2.pp1943-1953.
L. De Kimpe, M. Walrave, W. Hardyns, L. Pauwels, and K. Ponnet, “You’ve got mail! Explaining individual differences in becoming a phishing target,” Telemat. Informatics, vol. 35, no. 5, pp. 1277–1287, Aug. 2018, doi: 10.1016/j.tele.2018.02.009.
M. I. Akazue, R. E. Yoro, B. O. Malasowe, O. Nwankwo, and A. A. Ojugo, “Improved services traceability and management of a food value chain using block-chain network : a case of Nigeria,” Indones. J. Electr. Eng. Comput. Sci., vol. 29, no. 3, pp. 1623–1633, 2023, doi: 10.11591/ijeecs.v29.i3.pp1623-1633.
R. Brause, F. Hamker, and J. Paetz, “Septic Shock Diagnosis by Neural Networks and Rule Based Systems,” 2002, pp. 323–356. doi: 10.1007/978-3-7908-1788-1_12.
S. M. Albladi and G. R. S. Weir, “User characteristics that influence judgment of social engineering attacks in social networks,” Human-centric Comput. Inf. Sci., vol. 8, no. 1, p. 5, Dec. 2018, doi: 10.1186/s13673-018-0128-7.
A. Algarni, Y. Xu, and T. Chan, “An empirical study on the susceptibility to social engineering in social networking sites: the case of Facebook,” Eur. J. Inf. Syst., vol. 26, no. 6, pp. 661–687, Nov. 2017, doi: 10.1057/s41303-017-0057-y.
M. Al-Qatf, Y. Lasheng, M. Al-Habib, and K. Al-Sabahi, “Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection,” IEEE Access, vol. 6, pp. 52843–52856, 2018, doi: 10.1109/ACCESS.2018.2869577.
E. R. Altman, “Synthesizing Credit Card Transactions,” Oct. 2019, [Online]. Available: http://arxiv.org/abs/1910.03033
I. Correia, F. Fournier, and I. Skarbovsky, “The uncertain case of credit card fraud detection,” in Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, Jun. 2015, pp. 181–192. doi: 10.1145/2675743.2771877.
J. R. Amalraj and R. Lourdusamy, “A Novel distributed token-based algorithm using secret sharing scheme for secure data access control,” Int. J. Comput. Networks Appl., vol. 9, no. 4, p. 374, Aug. 2022, doi: 10.22247/ijcna/2022/214501.
I. A. Anderson and W. Wood, “Habits and the electronic herd: The psychology behind social media’s successes and failures,” Consum. Psychol. Rev., vol. 4, no. 1, pp. 83–99, Jan. 2021, doi: 10.1002/arcp.1063.
M. Barlaud, A. Chambolle, and J.-B. Caillau, “Robust supervised classification and feature selection using a primal-dual method,” Feb. 2019.
A. A. Ojugo and O. D. Otakore, “Mitigating Social Engineering menace in Nigerian Universities,” J. Comput. Sci. Appl., vol. 6, no. 2, pp. 64–68, 2018, doi: 10.12691/jcsa-6-2-2.
A. A. Ojugo, A. Osika, I. J. Iyawa, and M. O. Yerokun, “Information and communication technology integration into science, technology, engineering and mathematic (STEM) in Nigeria,” West African J. Ind. Acad. Res., vol. 4, no. 1, pp. 22–30, 2012, [Online]. Available: https://www.ajol.info/index.php/wajiar/article/view/86904/76697
M. Fatahi, M. Ahmadi, A. Ahmadi, M. Shahsavari, and P. Devienne, “Towards an spiking deep belief network for face recognition application,” in 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), Oct. 2016, pp. 153–158. doi: 10.1109/ICCKE.2016.7802132.
M. Gratian, S. Bandi, M. Cukier, J. Dykstra, and A. Ginther, “Correlating human traits and cyber security behavior intentions,” Comput. Secur., vol. 73, pp. 345–358, Mar. 2018, doi: 10.1016/j.cose.2017.11.015.
C. Li, N. Ding, H. Dong, and Y. Zhai, “Application of Credit Card Fraud Detection Based on CS-SVM,” Int. J. Mach. Learn. Comput., vol. 11, no. 1, pp. 34–39, 2021, doi: 10.18178/ijmlc.2021.11.1.1011.
S. Goel, K. Williams, and E. Dincelli, “Got Phished? Internet Security and Human Vulnerability,” J. Assoc. Inf. Syst., vol. 18, no. 1, pp. 22–44, Jan. 2017, doi: 10.17705/1jais.00447.
T. Halevi, J. Lewis, and N. Memon, “A pilot study of cyber security and privacy related behavior and personality traits,” in Proceedings of the 22nd International Conference on World Wide Web, May 2013, pp. 737–744. doi: 10.1145/2487788.2488034.
A. A. Ojugo, D. A. Oyemade, D. Allenotor, O. B. Longe, and C. N. Anujeonye, “Comparative Stochastic Study for Credit-Card Fraud Detection Models,” African J. Comput. ICT, vol. 8, no. 1, pp. 15–24, 2015, [Online]. Available: www.ajocict.net
M. I. Akazue, A. A. Ojugo, R. E. Yoro, B. O. Malasowe, and O. Nwankwo, “Empirical evidence of phishing menace among undergraduate smartphone users in selected universities in Nigeria,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 3, pp. 1756–1765, Dec. 2022, doi: 10.11591/ijeecs.v28.i3.pp1756-1765.
A. A. Ojugo and O. D. Otakore, “Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks,” IAES Int. J. Artif. Intell., vol. 9, no. 3, p. 497~506, 2020, doi: 10.11591/ijai.v9.i3.pp497-506.
Y. Gao, S. Zhang, J. Lu, Y. Gao, S. Zhang, and J. Lu, “Machine Learning for Credit Card Fraud Detection,” in Proceedings of the 2021 International Conference on Control and Intelligent Robotics, Jun. 2021, pp. 213–219. doi: 10.1145/3473714.3473749.
C. L. Udeze, I. E. Eteng, and A. E. Ibor, “Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection,” J. Niger. Soc. Phys. Sci., p. 769, Aug. 2022, doi: 10.46481/jnsps.2022.769.
A. E. Ibor, E. B. Edim, and A. A. Ojugo, “Secure Health Information System with Blockchain Technology,” J. Niger. Soc. Phys. Sci., vol. 5, no. 992, pp. 1–8, 2023, doi: 10.46481/jnsps.2022.992.
A. A. Ojugo, A. O. Eboka, E. O. Okonta, R. E. Yoro, and F. O. Aghware, “Predicting Behavioural Evolution on a Graph-Based Model,” Adv. Networks, vol. 3, no. 2, p. 8, 2015, doi: 10.11648/j.net.20150302.11.
S. K. Stevens, “Tracing the Food Safety Laws and Regulations Governing Traceability: A Brief History of Food Safety and Traceability Regulation,” in Food Traceability, Cham: Springer International Publishing, 2019, pp. 13–26. doi: 10.1007/978-3-030-10902-8_2.
Y. Abakarim, M. Lahby, and A. Attioui, “An Efficient Real Time Model For Credit Card Fraud Detection Based On Deep Learning,” in Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications, Oct. 2018, pp. 1–7. doi: 10.1145/3289402.3289530.
A. Abbasi, F. M. Zahedi, and Y. Chen, “Phishing susceptibility: The good, the bad, and the ugly,” in 2016 IEEE Conference on Intelligence and Security Informatics (ISI), Sep. 2016, pp. 169–174. doi: 10.1109/ISI.2016.7745462.
A. A. Ojugo and E. O. Ekurume, “Deep Learning Network Anomaly-Based Intrusion Detection Ensemble For Predictive Intelligence To Curb Malicious Connections: An Empirical Evidence,” Int. J. Adv. Trends Comput. Sci. Eng., vol. 10, no. 3, pp. 2090–2102, Jun. 2021, doi: 10.30534/ijatcse/2021/851032021.
D. Mao, F. Wang, Z. Hao, and H. Li, “Credit Evaluation System Based on Blockchain for Multiple Stakeholders in the Food Supply Chain,” Int. J. Environ. Res. Public Health, vol. 15, no. 8, p. 1627, Aug. 2018, doi: 10.3390/ijerph15081627.
H. Tingfei, C. Guangquan, and H. Kuihua, “Using Variational Auto Encoding in Credit Card Fraud Detection,” IEEE Access, vol. 8, pp. 149841–149853, 2020, doi: 10.1109/ACCESS.2020.3015600.
D. Huang, Y. Lin, Z. Weng, and J. Xiong, “Decision Analysis and Prediction Based on Credit Card Fraud Data,” in The 2nd European Symposium on Computer and Communications, Apr. 2021, pp. 20–26. doi: 10.1145/3478301.3478305.
E. Ileberi, Y. Sun, and Z. Wang, “A machine learning based credit card fraud detection using the GA algorithm for feature selection,” J. Big Data, vol. 9, no. 1, p. 24, Dec. 2022, doi: 10.1186/s40537-022-00573-8.
T. Edirisooriya and E. Jayatunga, “Comparative Study of Face Detection Methods for Robust Face Recognition Systems,” 5th SLAAI - Int. Conf. Artif. Intell. 17th Annu. Sess. SLAAI-ICAI 2021, no. December, 2021, doi: 10.1109/SLAAI-ICAI54477.2021.9664689.
M. Laavanya and V. Vijayaraghavan, “Real Time Fake Currency Note Detection using Deep Learning,” Int. J. Eng. Adv. Technol., vol. 9, no. 1S5, pp. 95–98, 2019, doi: 10.35940/ijeat.a1007.1291s52019.
A. A. Ojugo, A. O. Eboka, R. E. Yoro, M. O. Yerokun, and F. N. Efozia, “Framework design for statistical fraud detection,” Math. Comput. Sci. Eng. Ser., vol. 50, pp. 176–182, 2015.
A. A. Ojugo and R. E. Yoro, “Predicting Futures Price And Contract Portfolios Using The ARIMA Model: A Case of Nigeria’s Bonny Light and Forcados,” Quant. Econ. Manag. Stud., vol. 1, no. 4, pp. 237–248, 2020, doi: 10.35877/454ri.qems139.
A. A. Ojugo, R. E. Yoro, E. O. Okonta, and A. O. Eboka, “A Hybrid Artificial Neural Network Gravitational Search Algorithm for Rainfall Runoffs Modeling and Simulation in Hydrology,” Prog. Intell. Comput. Appl., vol. 2, no. 1, pp. 22–34, 2013, doi: 10.4156/pica.vol2.issue1.2.
V. Filippov, L. Mukhanov, and B. Shchukin, “Credit card fraud detection system,” in 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, Sep. 2008, pp. 1–6. doi: 10.1109/UKRICIS.2008.4798919.
A. A. Ojugo, M. I. Akazue, P. O. Ejeh, C. Odiakaose, and F. U. Emordi, “DeGATraMoNN : Deep Learning Memetic Ensemble to Detect Spam Threats via a Content-Based Processing,” Kongzhi yu Juece/Control Decis., vol. 38, no. 01, pp. 667–678, 2023.
G. M. Friesen, T. C. Jannett, M. A. Jadallah, S. L. Yates, S. R. Quint, and H. T. Nagle, “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. Biomed. Eng., vol. 37, no. 1, pp. 85–98, 1990, doi: 10.1109/10.43620.
A. A. Ojugo and A. O. Eboka, “Empirical Bayesian network to improve service delivery and performance dependability on a campus network,” IAES Int. J. Artif. Intell., vol. 10, no. 3, p. 623, Sep. 2021, doi: 10.11591/ijai.v10.i3.pp623-635.
S. Nosratabadi, F. Imre, K. Szell, S. Ardabili, B. Beszedes, and A. Mosavi, “Hybrid Machine Learning Models for Crop Yield Prediction,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2005.04155
A. A. Ojugo and O. D. Otakore, “Computational solution of networks versus cluster grouping for social network contact recommender system,” Int. J. Informatics Commun. Technol., vol. 9, no. 3, p. 185, 2020, doi: 10.11591/ijict.v9i3.pp185-194.
G. Behboud, “Reasoning using Modular Neural Network,” Towar. Data Sci., vol. 34, no. 2, pp. 12–34, 2020.
X. Lin, P. R. Spence, and K. A. Lachlan, “Social media and credibility indicators: The effect of influence cues,” Comput. Human Behav., vol. 63, pp. 264–271, Oct. 2016, doi: 10.1016/j.chb.2016.05.002.
F. O. Aghware, R. E. Yoro, P. O. Ejeh, C. C. Odiakaose, F. U. Emordi, and A. A. Ojugo, “Sentiment analysis in detecting sophistication and degradation cues in malicious web contents,” Kongzhi yu Juece/Control Decis., vol. 38, no. 01, p. 653, 2023.
R. J. Urbanowicz, M. Meeker, W. La Cava, R. S. Olson, and J. H. Moore, “Relief-based feature selection: Introduction and review,” J. Biomed. Inform., vol. 85, pp. 189–203, Sep. 2018, doi: 10.1016/j.jbi.2018.07.014.
M. Zanin, M. Romance, S. Moral, and R. Criado, “Credit Card Fraud Detection through Parenclitic Network Analysis,” Complexity, vol. 2018, pp. 1–9, 2018, doi: 10.1155/2018/5764370.
K. Kuwata and R. Shibasaki, “Estimating crop yields with deep learning and remotely sensed data,” in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul. 2015, pp. 858–861. doi: 10.1109/IGARSS.2015.7325900.
Z. Karimi, M. Mansour Riahi Kashani, and A. Harounabadi, “Feature Ranking in Intrusion Detection Dataset using Combination of Filtering Methods,” Int. J. Comput. Appl., vol. 78, no. 4, pp. 21–27, Sep. 2013, doi: 10.5120/13478-1164.
S. Khaki and L. Wang, “Crop Yield Prediction Using Deep Neural Networks,” Front. Plant Sci., vol. 10, May 2019, doi: 10.3389/fpls.2019.00621.
S. Khaki, L. Wang, and S. V. Archontoulis, “A CNN-RNN Framework for Crop Yield Prediction,” Front. Plant Sci., vol. 10, Jan. 2020, doi: 10.3389/fpls.2019.01750.
S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, “Random forest for credit card fraud detection,” in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Mar. 2018, pp. 1–6. doi: 10.1109/ICNSC.2018.8361343.
M. Zareapoor and P. Shamsolmoali, “Application of Credit Card Fraud Detection: Based on Bagging Ensemble Classifier,” Procedia Comput. Sci., vol. 48, pp. 679–685, 2015, doi: 10.1016/j.procs.2015.04.201.
N. Valaei, S. R. Nikhashemi, H. Ha Jin, and M. M. Dent, “Task Technology Fit in Online Transaction Through Apps,” in Optimizing e-participation initiatives via social media, IGI Global, 2018, pp. 236–251. doi: 10.4018/978-1-5225-5326-7.ch010.
I. Sohony, R. Pratap, and U. Nambiar, “Ensemble learning for credit card fraud detection,” in Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, Jan. 2018, pp. 289–294. doi: 10.1145/3152494.3156815.
A. Vishwanath, “Habitual Facebook Use and its Impact on Getting Deceived on Social Media,” J. Comput. Commun., vol. 20, no. 1, pp. 83–98, Jan. 2015, doi: 10.1111/jcc4.12100.
E. O. Yeboah-Boateng and P. M. Amanor, “Phishing , SMiShing & Vishing : An Assessment of Threats against Mobile Devices,” J. Emerg. Trends Comput. Inf. Sci., vol. 5, no. 4, pp. 297–307, 2014.
S. S. Verma et al., “Collective feature selection to identify crucial epistatic variants,” BioData Min., vol. 11, no. 1, p. 5, Dec. 2018, doi: 10.1186/s13040-018-0168-6.
D. Zhang, B. Bhandari, and D. Black, “Credit Card Fraud Detection Using Weighted Support Vector Machine,” Appl. Math., vol. 11, no. 12, pp. 1275–1291, 2020, doi: 10.4236/am.2020.1112087.
S. Yuan and X. Wu, “Deep learning for insider threat detection: Review, challenges and opportunities,” Comput. Secur., vol. 104, 2021, doi: 10.1016/j.cose.2021.102221.
G. G. Akin, A. F. Aysan, G. I. Kara, and L. Yildiran, “The failure of price competition in the Turkish credit card market,” Emerg. Mark. Financ. Trade, vol. 46, no. SUPPL. 1, pp. 23–35, 2010, doi: 10.2753/REE1540-496X4603S102.
H. Yildiz Durak, “Human Factors and Cybersecurity in Online Game Addiction: An Analysis of the Relationship Between High School Students’ Online Game Addiction and the State of Providing Personal Cybersecurity and Representing Cyber Human Values in Online Games,” Soc. Sci. Q., vol. 100, no. 6, pp. 1984–1998, Oct. 2019, doi: 10.1111/ssqu.12693.
A. Jayatilaka, N. A. G. Arachchilage, and M. A. Babar, “Falling for Phishing: An Empirical Investigation into People’s Email Response Behaviors,” arXiv Prepr. arXiv …, no. Fbi 2020, pp. 1–17, 2021.
P. Kumaraguru, S. Sheng, A. Acquisti, L. F. Cranor, and J. Hong, “Teaching Johnny not to fall for phish,” ACM Trans. Internet Technol., vol. 10, no. 2, pp. 1–31, May 2010, doi: 10.1145/1754393.1754396.
S. V. S. . Lakshimi and S. D. Kavila, “Machine Learning for Credit Card Fraud Detection System,” Int. J. Appl. Eng. Res., vol. 15, no. 24, pp. 16819–16824, 2018, doi: 10.1007/978-981-33-6893-4_20.
D. Nahavandi, R. Alizadehsani, A. Khosravi, and U. R. Acharya, “Application of artificial intelligence in wearable devices: Opportunities and challenges,” Comput. Methods Programs Biomed., vol. 213, p. 106541, Jan. 2022, doi: 10.1016/j.cmpb.2021.106541.
G. Sasikala et al., “An Innovative Sensing Machine Learning Technique to Detect Credit Card Frauds in Wireless Communications,” Wirel. Commun. Mob. Comput., vol. 2022, pp. 1–12, Jun. 2022, doi: 10.1155/2022/2439205.
O. V. Lee et al., “A malicious URLs detection system using optimization and machine learning classifiers,” Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 3, p. 1210, Mar. 2020, doi: 10.11591/ijeecs.v17.i3.pp1210-1214.
D. Wang, B. Chen, and J. Chen, “Credit card fraud detection strategies with consumer incentives,” Omega, vol. 88, pp. 179–195, Oct. 2019, doi: 10.1016/j.omega.2018.07.001.
T. Ma, F. Wang, J. Cheng, Y. Yu, and X. Chen, “A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks,” Sensors, vol. 16, no. 10, p. 1701, Oct. 2016, doi: 10.3390/s16101701.
T. Sahmoud and D. M. Mikki, “Spam Detection Using BERT,” Jun. 2022, doi: 10.48550/arXiv.2206.02443.
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Copyright (c) 2023 Arnold Adimabua Ojugo, Maureen Ifeanyi Akazue, Patrick Ogholuwarami Ejeh, Nwanze Chukwudi Ashioba, Christopher Chukwufunaya Odiakaose, Rita Erhovwo Ako, Frances Uche Emordi
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