Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset-Reference-Cited by-同舟云学术

Generative Adversarial Networks-Based Novel Approach for Fraud Detection for the European Cardholders 2013 Dataset

Published:2023 Issue: Volume:11 Page:107348-107368
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Almarshad Fahdah A.¹^ORCID,Gashgari Ghada Abdalaziz²,Alzahrani Abdullah I. A.³^ORCID

Affiliation:

1. Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia

2. Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Al Faisaliyyah, Jeddah, Saudi Arabia

3. Department of Computer Science, College of Science and Humanities in Al Quwaiiyah, Shaqra University, Shaqra, Saudi Arabia

Funder

Deputyship for Research and Innovation, Ministry of Education, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10265011.pdf?arnumber=10265011

Reference39 articles.

1. A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

2. Credit Card Fraud Detection using Pipeling and Ensemble Learning

3. Network load prediction and anomaly detection using ensemble learning in 5G cellular networks

4. Intelligent feature selection with social spider optimization based artificial neural network model for credit card fraud detection;arun;IIOABJ,2020

5. Semi-supervised Deep Learning-Driven Anomaly Detection Schemes for Cyber-Attack Detection in Smart Grids

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