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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Science, Engineering, Technology, and Education JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology
Ojugo, Arnold Adimabua
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Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Other

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Journal : ARRUS Journal of Engineering and Technology

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Quest For Convergence Solution Using Hybrid Genetic Algorithm Trained Neural Network Model For Metamorphic Malware Detection Ojugo, Arnold Adimabua; Obruche, Chris Obaro; Eboka, Andrew Okonji
ARRUS Journal of Engineering and Technology Vol. 2 No. 1 (2022)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/jetech613

Abstract

An unstable economy is rife with fraud. Perpetrated on customers, it ranges from employees’ internal abuse to large fraud via high-value contracts cum control breaches that impose serious consequences to biz. Loyal employees may not perpetrate fraud if not for societal pressures and economic recession with its rationalization that they have bills to pay and children to feed. Thus, the need for financial institutions to embark on effective measures via schemes that will aids both fraud prevention and detection. Study proposes genetic algorithm trained neural net model to accurately classify credit card transactions. Compared, model used a rule-based system to provide it with start-up solution and it has a fraud catching rate of 91% with a consequent, false alarm rate of 9%. Its convergence time is found to depend on how close the initial solution space is to the fitness function, and for recombination and mutation rates applied.
Co-Authors Eboka, Andrew Okonji Nwankwo, Obinna Obruche, Chris Obaro Otakore, Oghenevwede Debby Yoro, Rume Elizabeth