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Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards

Gorde:
Argitaratua izan da:Applied Sciences vol. 15, no. 3 (2025), p. 1081
Egile nagusia: Btoush, Eyad
Beste egile batzuk: Zhou, Xujuan, Gururajan, Raj, Chan, Ka Ching, Alsodi, Omar
Argitaratua:
MDPI AG
Gaiak:
Machine learning
Accuracy
Datasets
Deep learning
Algorithms
Artificial intelligence
Cybercrime
Credit card processing
Credit card fraud
Fraud prevention
Support vector machines
Sarrera elektronikoa:Citation/Abstract
Full Text + Graphics
Full Text - PDF
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022 |a 2076-3417 
024 7 |a 10.3390/app15031081  |2 doi 
035 |a 3165777367 
045 2 |b d20250101  |b d20251231 
084 |a 231338  |2 nlm 
100 1 |a Btoush, Eyad  |u School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; <email>xujuan.zhou@unisq.edu.au</email> (X.Z.); <email>raj.gururajan@unisq.edu.au</email> (R.G.); <email>kc.chan@unisq.edu.au</email> (K.C.C.); <email>omar.alsodi@unisq.edu.au</email> (O.A.) 
245 1 |a Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The rapid advancement of technology has increased the complexity of cyber fraud, presenting a growing challenge for the banking sector to efficiently detect fraudulent credit card transactions. Conventional detection approaches face challenges in adapting to the continuously evolving tactics of fraudsters. This study addresses these limitations by proposing an innovative hybrid model that integrates Machine Learning (ML) and Deep Learning (DL) techniques through a stacking ensemble and resampling strategies. The hybrid model leverages ML techniques including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Logistic Regression (LR) alongside DL techniques such as Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM) with attention mechanisms. By utilising the stacking ensemble method, the model consolidates predictions from multiple base models, resulting in improved predictive accuracy compared to individual models. The methodology incorporates robust data pre-processing techniques. Experimental evaluations demonstrate the superior performance of the hybrid ML+DL model, particularly in handling class imbalances and achieving a high F1 score, achieving an F1 score of 94.63%. This result underscores the effectiveness of the proposed model in delivering reliable cyber fraud detection, highlighting its potential to enhance financial transaction security. 
653 |a Machine learning 
653 |a Accuracy 
653 |a Datasets 
653 |a Deep learning 
653 |a Algorithms 
653 |a Artificial intelligence 
653 |a Cybercrime 
653 |a Credit card processing 
653 |a Credit card fraud 
653 |a Fraud prevention 
653 |a Support vector machines 
700 1 |a Zhou, Xujuan  |u School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; <email>xujuan.zhou@unisq.edu.au</email> (X.Z.); <email>raj.gururajan@unisq.edu.au</email> (R.G.); <email>kc.chan@unisq.edu.au</email> (K.C.C.); <email>omar.alsodi@unisq.edu.au</email> (O.A.) 
700 1 |a Gururajan, Raj  |u School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; <email>xujuan.zhou@unisq.edu.au</email> (X.Z.); <email>raj.gururajan@unisq.edu.au</email> (R.G.); <email>kc.chan@unisq.edu.au</email> (K.C.C.); <email>omar.alsodi@unisq.edu.au</email> (O.A.); School of Computing, SRM Institute of Science and Technology, Chennai 603203, India 
700 1 |a Chan, Ka Ching  |u School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; <email>xujuan.zhou@unisq.edu.au</email> (X.Z.); <email>raj.gururajan@unisq.edu.au</email> (R.G.); <email>kc.chan@unisq.edu.au</email> (K.C.C.); <email>omar.alsodi@unisq.edu.au</email> (O.A.) 
700 1 |a Alsodi, Omar  |u School of Business, University of Southern Queensland (UniSQ), Springfield, QLD 4300, Australia; <email>xujuan.zhou@unisq.edu.au</email> (X.Z.); <email>raj.gururajan@unisq.edu.au</email> (R.G.); <email>kc.chan@unisq.edu.au</email> (K.C.C.); <email>omar.alsodi@unisq.edu.au</email> (O.A.) 
773 0 |t Applied Sciences  |g vol. 15, no. 3 (2025), p. 1081 
786 0 |d ProQuest  |t Publicly Available Content Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3165777367/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3165777367/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3165777367/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch