Efficiency of Federated Learning and Blockchain in Preserving Privacy and Enhancing the Performance of Credit Card Fraud Detection (CCFD) Systems

Tahani Baabdullah (), Amani Alzahrani, Danda B. Rawat () and Chunmei Liu
Additional contact information
Tahani Baabdullah: Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA
Amani Alzahrani: Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA
Danda B. Rawat: Data Science and Cybersecurity Center (DSC2), Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA
Chunmei Liu: Department of Electrical Engineering and Computer Science, Howard University, Washington, DC 20059, USA

Future Internet, 2024, vol. 16, issue 6, 1-22

Abstract: Increasing global credit card usage has elevated it to a preferred payment method for daily transactions, underscoring its significance in global financial cybersecurity. This paper introduces a credit card fraud detection (CCFD) system that integrates federated learning (FL) with blockchain technology. The experiment employs FL to establish a global learning model on the cloud server, which transmits initial parameters to individual local learning models on fog nodes. With three banks (fog nodes) involved, each bank trains its learning model locally, ensuring data privacy, and subsequently sends back updated parameters to the global learning model. Through the integration of FL and blockchain, our system ensures privacy preservation and data protection. We utilize three machine learning and deep neural network learning algorithms, RF, CNN, and LSTM, alongside deep optimization techniques such as ADAM, SGD, and MSGD. The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy.

Keywords: credit card fraud; financial fraud; fraud detection; federated learning; blockchain; fog computing; cybersecurity; privacy preservation; data protection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1999-5903/16/6/196/pdf (application/pdf)
https://www.mdpi.com/1999-5903/16/6/196/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:16:y:2024:i:6:p:196-:d:1407317

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().