Ojugo, Arnold Adimabua and Akazue, Maureen Ifeanyi and Ejeh, Patrick Ogholuwarami and Ashioba, Nwanze Chukwudi and Odiakaose, Christopher Chukwufunaya and Ako, Rita Erhovwo and Emordi, Frances Uche (2023) Forging a User-Trust Memetic Modular Neural Network Card Fraud Detection Ensemble: A Pilot Study. Journal of Computing Theories and Applications, 1 (2). pp. 50-60. ISSN 3024-9104
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Abstract
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%.
Item Type: | Article |
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Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Depositing User: | dl fts |
Date Deposited: | 23 Nov 2024 05:03 |
Last Modified: | 23 Nov 2024 05:03 |
URI: | https://dl.futuretechsci.org/id/eprint/18 |