Machine learning for mobile network payment security evaluation system

Abstract

AbstractIn the recent past, different types of Mobile Network payments gateways have been explosively growing, which allows consumers to access services using various types of mobile devices. The demanding, challenging factors in the mobile network payment gateway security evaluation system includes malware detection, multi‐factor authentication, and fraudulent detection in payment systems. In this paper, Machine Learning‐Assisted Secure Mobile Electronic Payment Framework (ML‐SMEPF) is proposed to detect the presence of malware, authentication issues, and fraud detection in mobile transactions. Here, the Efficient Random Oracle Model is introduced to detect the presence of malware on a host system and multi‐factor authentication challenges posed during mobile payments. Mutual Mobile Authentication model is incorporated with ML‐SMEPF, to identify the type of fraud detection which ensures a safe and secure mobile payment platform. The simulation analysis is performed based on accuracy ratio, security factor, performance, and cost factor proves the reliability of the proposed framework.