Journal of Cybersecurity and Information Management
JCIM
2690-6775
2769-7851
10.54216/JCIM
https://www.americaspg.com/journals/show/3044
2019
2019
Credit Card Fraud Detection Model Based on Correlation Feature Selection
Middle Technical University, Baghdad, Iraq
Ahmad
Ahmad
Middle Technical University, Baghdad, Iraq
Salah N.
Mjeat
University of Anbar, Anbar, Iraq
Daniah Abul Qahar
Shakir
The University of Jordan, Amman, Jordan
Mohammed Awad
Alfwair
Credit card fraud is a widespread cybercrime that threatens financial security. Effective cybersecurity measures are essential to mitigate these risks. Machine learning has shown promising results in detecting credit card fraud by analyzing transaction data and identifying patterns of suspicious behavior. Feature selection is crucial in machine learning because it simplifies the model, improves its performance, and prevents overfitting. This research introduces a machine learning model designed for credit card fraud detection. The model makes use of three types of correlations. Pearson, Spearman, and Kendall, to identify features and enhance the fraud detection process. Testing on datasets yielded impressive results achieving category accuracies of 99.95% and 99.58% surpassing alternative approaches. Also, the results showed that Kendall correlation is the best among the three types of correlation in selecting attributes in all approved datasets.
2024
2024
334
342
10.54216/JCIM.140224
https://www.americaspg.com/articleinfo/2/show/3044