Synergy Between Resilient Networks and Random Forests in Online Fraud Detection  

作　　者：Junxi Wang Ningtao Sun Yuhan Lv Jiayi Zhou Yue Xiao 

机构地区：[1]School of Intelligent Manufacturing and Aviation,Zhuhai College of Science and Technology,Zhuhai 519041,Guangdong,China [2]School of Computer Science,Zhuhai College of Science and Technology,Zhuhai 519041,Guangdong,China [3]School of Liberal Arts,Zhuhai College of Science and Technology,Zhuhai 519041,Guangdong,China [4]School of Logistics Management and Engineering,Zhuhai College of Science and Technology,Zhuhai 519041,Guangdong,China [5]School of Data Science,Zhuhai College of Science and Technology,Zhuhai 519041,Guangdong,China

出　　处：《Journal of Electronic Research and Application》2025年第2期43-50,共8页电子研究与应用

基　　金：Guangdong Innovation and Entrepreneurship Training Programme for Undergraduates“Automatic Classification and Identification of Fraudulent Websites Based on Machine Learning”(Project No.:DC2023125)。

摘　　要：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.

关 键 词：Fraudulent websites Machine learning Elastic Net Random forests 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP391.4[自动化与计算机技术—计算机科学与技术]