Federated Learning-Based Credit Card Fraud Detection: Performance Analysis with Sampling Methods and Deep Learning Algorithms

The exponential technological advancement is turning everyone towards an easy and efficient way of financial transactions. Consequently, the use of credit cards is rising substantively, creating a more incredible opportunity for fraudsters which is an alarming concern nowadays since a fraudster may...

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Bibliographic Details
Published in2023 IEEE International Conference on Cyber Security and Resilience (CSR) pp. 180 - 186
Main Authors Aurna, Nahid Ferdous, Hossain, Md Delwar, Taenaka, Yuzo, Kadobayashi, Youki
Format Conference Proceeding
LanguageEnglish
Published IEEE 31.07.2023
Subjects
CNN
Credit card fraud
Credit cards
Data models
Data privacy
Deep learning
Federated learning
fraud detection system
LSTM
MLP
Sampling methods
Turning
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DOI10.1109/CSR57506.2023.10224978

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Summary:The exponential technological advancement is turning everyone towards an easy and efficient way of financial transactions. Consequently, the use of credit cards is rising substantively, creating a more incredible opportunity for fraudsters which is an alarming concern nowadays since a fraudster may use several tools, techniques and tactics to make a fraudulent transaction. As a countermeasure, an effective fraud detection mechanism and highly sensitive data privacy preservation are imperative to detect fraudulent transactions. This paper proposes a Federated Learning (FL)-based fraud detection system since its key feature preserves the privacy of highly sensitive data, wherein the model could be trained without sharing the credit card data in the cloud. We contemplate three Deep Learning (DL) models: Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) regarding the FL approach. Subsequently, to overcome the data imbalance issue, four distinct sampling techniques are explored to inspect the impact on the traditional centralized and FL approaches. Finally, we further investigate and compare FL-based detection systems with diversified state-of-the-art models. Our experimental results demonstrate that the proposed method is superior compared with state-of-the-art methods and achieves high detection rate of 99.51%, 98.77% and 98.20% respectively for CNN, MLP and LSTM models.
DOI:10.1109/CSR57506.2023.10224978