Identification Of Vulnerability During Cross Border Transaction in IoT

Abstract

The revolution of Internet of Things (IoT) has been triggering demands for the IoT devices in market. IoTs are actively utilized in various social activities that enables the concept of industry 4.0 ecosystem. Conventional models for the detection of traditional models involves the vulnerability analysis, where the decision is carried out using rules embedding into the models. The fraudulent behavior is not reported in case of frequent transactions across cross-borders. In this paper, we develop a machine learning model that is framed as a predictive big data analytics model that solves the problems associated with vulnerability in transactions across cross-border. The study takes into concern various business problems by banks associated with cross-border transactions with its historical data. The machine learning models help banks to captures the details of fraudulent behavior in transactions. The simulation for predictive learning is induced by the machine learning algorithm that uses historical data logs to train the classifier and thereby a model is developed to predict the fraudulent transactions. The simulation results show that the proposed method enables better assessment on finding the vulnerability than the existing methods.