Open AccessAn Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication
Author(s) -
Sumaya Sanober,
Izhar Alam,
Sagar Pande,
Farrukh Arslan,
Kantilal Pitambar Rane,
Bhupesh Kumar Singh,
Aditya Khamparia,
Mohammad Shabaz
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/6079582
Subject(s) - computer science , database transaction , credit card fraud , decision tree , random forest , algorithm , the internet , machine learning , anomaly detection , support vector machine , financial transaction , computer security , credit card , artificial intelligence , database , world wide web , payment
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.