An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Sumaya Sanober,Mohammad Shabaz,Bhupesh Kumar Singh,Farrukh Arslan,Izhar Alam,Kantilal Pitambar Rane,Sagar Pande,Aditya Khamparia,Vimal Shanmuganathan

doi:10.1155/2021/6079582

Abstract

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

Highlights

Credit card fraud might be a significant issue which requires payment card as Mastercard as illegal supply of money in transactions
Many learning algorithms are offered for fraud detection in Mastercard that features neural networks, logistic regression (LR), Naive Bayes (NB), Support Vector Machines (SVM), decision tree (DT), and K-nearest neighbors (KNN) as well as random forest (RF)
It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection

Summary

Introduction

Credit card fraud might be a significant issue which requires payment card as Mastercard as illegal supply of money in transactions. Fraud is illegal because of getting funds and goods The objective of such unlawful transaction might be urging items without paying and obtain an unauthorized fund from an account. Identifying such fraud might be a troublesome and must risk the company as well as business organizations. Supervised learning algorithm uses labeled transactions for instructing the classifier whereas unsupervised mastering algorithm uses coeval’s analysis that groups customers in line with the profile of theirs and identifies fraud supported clients spending behavior. Many learning algorithms are offered for fraud detection in Mastercard that features neural networks, logistic regression (LR), Naive Bayes (NB), Support Vector Machines (SVM), decision tree (DT), and K-nearest neighbors (KNN) as well as random forest (RF). The conclusion and future scope are explained

Literature Survey

Methodology

Experiment and Result Analysis

Findings

Conclusion