Synergy Between Resilient Networks and Random Forests in Online Fraud Detection

Abstract

This paper explores the synergistic effect of a model combining Elastic Net and Random Forest in online fraud detection. The study selects a public network dataset containing 1781 data records, divides the dataset by 70% for training and 30% for validation, and analyses the correlation between features using a correlation matrix. The experimental results show that the Elastic Net feature selection method generally outperforms PCA in all models, especially when combined with the Random Forest and XGBoost models, and the ElasticNet + Random Forest model achieves the highest accuracy of 0.968 and AUC value of 0.983, while the Kappa and MCC also reached 0.839 and 0.844 respectively, showing extremely high consistency and correlation. This indicates that combining Elastic Net feature selection and Random Forest model has significant performance advantages in online fraud detection.