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Real-time Fraud Detection in e-Market Using Machine Learning Algorithms - 2021

Research Area:  Machine Learning

Abstract:

An electronic market (e-market) is an online platform where people buy or sell products. Problems like fraud detection and illegal activity have risen together with the rising growth of the e-market. The efficacy of the fraud prevention methods of purchases has a significant bearing on the depletion of internet customers. Therefore in this paper, a support vector machine-based fraud detection framework (SVM-FDF) has been proposed for detecting real-time fraud in the e-market. FD framework is implemented to spread prominence from a limited marketing scheme for beginning consumers is invariably used to update their credibility when an offering is applied to the e-market. The comportment features of all existing regular cases and fraud specimens are derived via the clustering algorithm to form the general conduct of the present community of the e-market. Each conducts findings demonstrate that the SVM model is employed to evaluate whether all the present transaction is corrupted or fraud. The simulation results show that the suggested SVMFDF model enhances the precision rate of 98.8%, recall rate of 97.7%, the f1-score ratio of 96.7%, accuracy ratio of 96.8%, and decreases the error rate of 20.9% compared to other existing approaches.

Author(s) Name:  YANJIAO DONG; ZHENGFENG JIANG; ALAZAB, MAMOUN; KUMAR, PRIYAN MALARVIZHI

Journal name:  Journal of Multiple-Valued Logic & Soft Computing

Conferrence name:  

Publisher name:  EBSCO

DOI:  

Volume Information:  2021, Vol. 36 Issue 1-3, p191-209. 19p.