aspg-2-3044-1791417098 20261007235138 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Cybersecurity and Information Management JCIM 2769-7851 2690-6775 2024 14 2 Ahmad Salim Middle Technical University, Baghdad, Iraq Salah N. Mjeat Middle Technical University, Baghdad, Iraq Daniah Abul Qahar Shakir University of Anbar, Anbar, Iraq Mohammed Awad Alfwair The University of Jordan, Amman, Jordan 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 334 342 3044 https://creativecommons.org/licenses/by/4.0/ 10.54216/JCIM.140224 https://www.americaspg.com/journal/2/article/3044 https://www.americaspg.com/storage/01720997060.pdf