Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

Authors

  • Amusan Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Olabode A.O Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Ojo Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • O.S Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • Folowosele Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.
  • A.O Department of Computer Science and Engineering, Ladoke Akintola University of Technology, Ogbomoso, Nigeria.

DOI:

https://doi.org/10.31695/IJASRE.2019.33512

Keywords:

Anomaly Detection, Counter propagation neural network, Credit card fraud, Genetic algorithm, Model, Online transactions.

Abstract

In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (CPNN-GA) for the detection of anomaly in any online transactions.

Downloads

Published

2019-09-04

How to Cite

Amusan, A.O, O., Ojo, O.S, Folowosele, & A.O. (2019). Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction. International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE, 5(9), 107–114. https://doi.org/10.31695/IJASRE.2019.33512