ARRUS Journal of Engineering and Technology
Vol. 2 No. 1 (2022)

Quest For Convergence Solution Using Hybrid Genetic Algorithm Trained Neural Network Model For Metamorphic Malware Detection

Ojugo, Arnold Adimabua (Unknown)
Obruche, Chris Obaro (Unknown)
Eboka, Andrew Okonji (Unknown)

Article Info

Publish Date
27 Nov 2021

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.

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Journal Info
ARRUS Journal of Engineering and Technology
Website

Abbrev

jetech

Publisher

Yayasan Ahmar Cendekia Indonesia

Subject

Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

Description

ARRUS Journal of Engineering and Technology preserves prompt publication of manuscripts that meet the broad-spectrum criteria of scientific excellence. Areas of interest include, but are not limited to: Aerospace Engineering Architecture Evaluations Automation and Mechatronics Engineering ...