Stolfo, S.J., Fan, W., Lee, W., Prodromidis, A., and Chan, P.K. (2000) Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project. Proceedings DARPA Information Survivability Conference and Exposition. DISCEX’00, Hilton Head, 25-27 January 2000, 130-144.

has been cited by the following article:

• TITLE: Credit Card Fraud Detection Using Machine Learning Techniques

  AUTHORS: Ananya Sarker, Must. Asma Yasmin, Md. Atikur Rahman, Md. Harun Or Rashid, Bristi Rani Roy

  KEYWORDS: Support Vector Machine, Decision Tree, Nave Bayes, Random Forest, Matthews Correlation

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.6, June 21, 2024

  ABSTRACT: Credit card companies must be able to identify fraudulent credit card transactions so that clients are not charged for items they did not purchase. Previously, many machine learning approaches and classifiers were used to detect fraudulent transactions. However, because fraud patterns are always changing, it is becoming increasingly vital to investigate new frauds and develop the model based on the new patterns. The purpose of this research is to create a machine learning classifier that not only detects fraud but also detects legitimate transactions. As a result, the model should have excellent accuracy, precision, recall, and f1-score. As a result, we began with a large dataset in this study and used four machine learning classifiers: Support Vector Machine (SVM), Decision Tree, Naïve Bayes, and Random Forest. The random forest classifier scored 99.96% overall accuracy with the best precision, recall, f1-score, and Matthews correlation coefficient in the experiments.