Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection
In an era marked by rapid technological advancements and a global shift toward cashless transactions, credit card fraud has emerged as a significant challenge, causing substantial financial losses and threatening the security of consumers and financial institutions. The exponential growth of online...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11050364/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849320356384342016 |
|---|---|
| author | Iman Akour Nour Mohamed Said Salloum |
| author_facet | Iman Akour Nour Mohamed Said Salloum |
| author_sort | Iman Akour |
| collection | DOAJ |
| description | In an era marked by rapid technological advancements and a global shift toward cashless transactions, credit card fraud has emerged as a significant challenge, causing substantial financial losses and threatening the security of consumers and financial institutions. The exponential growth of online payments necessitates more effective fraud detection mechanisms. Traditional fraud detection methods, including rule-based systems and basic statistical analyses, struggle to keep pace with the evolving strategies employed by fraudsters. This paper proposes a hybrid fraud detection model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism to address these challenges. Each component of the hybrid model is designed to capture specific behavioral representations, with CNNs focusing on spatial features, LSTMs handling temporal sequences, and the attention mechanism highlighting the most relevant features. We utilized a benchmark dataset and applied the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution. Extensive data preprocessing was conducted to ensure compatibility with the model’s input requirements. The experimental results demonstrated that our hybrid model significantly outperforms traditional machine learning algorithms, achieving an accuracy of 99.93% and a recall of 0.89 on the Credit Card Fraud dataset (CCF). These results highlight the model’s ability to detect fraudulent activities with high precision and reliability. Our hybrid CNN-LSTM-Attention model improves fraud detection by addressing both spatial and temporal data features while dynamically focusing on critical elements. This work not only contributes to the advancement of fraud detection techniques but also provides a framework for future research. |
| format | Article |
| id | doaj-art-88bf0a4da42e4dbf92a658540ea1580e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-88bf0a4da42e4dbf92a658540ea1580e2025-08-20T03:50:07ZengIEEEIEEE Access2169-35362025-01-011311405611406810.1109/ACCESS.2025.358325311050364Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud DetectionIman Akour0https://orcid.org/0000-0002-6914-2213Nour Mohamed1https://orcid.org/0000-0001-6304-2468Said Salloum2Department of Information Systems, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Electrical Engineering, University of Sharjah, Sharjah, United Arab EmiratesSchool of Computing, Skyline University College, Sharjah, United Arab EmiratesIn an era marked by rapid technological advancements and a global shift toward cashless transactions, credit card fraud has emerged as a significant challenge, causing substantial financial losses and threatening the security of consumers and financial institutions. The exponential growth of online payments necessitates more effective fraud detection mechanisms. Traditional fraud detection methods, including rule-based systems and basic statistical analyses, struggle to keep pace with the evolving strategies employed by fraudsters. This paper proposes a hybrid fraud detection model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an attention mechanism to address these challenges. Each component of the hybrid model is designed to capture specific behavioral representations, with CNNs focusing on spatial features, LSTMs handling temporal sequences, and the attention mechanism highlighting the most relevant features. We utilized a benchmark dataset and applied the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution. Extensive data preprocessing was conducted to ensure compatibility with the model’s input requirements. The experimental results demonstrated that our hybrid model significantly outperforms traditional machine learning algorithms, achieving an accuracy of 99.93% and a recall of 0.89 on the Credit Card Fraud dataset (CCF). These results highlight the model’s ability to detect fraudulent activities with high precision and reliability. Our hybrid CNN-LSTM-Attention model improves fraud detection by addressing both spatial and temporal data features while dynamically focusing on critical elements. This work not only contributes to the advancement of fraud detection techniques but also provides a framework for future research.https://ieeexplore.ieee.org/document/11050364/Attentionconvolutional neural networkscredit card frauddetectionlong-short term memory |
| spellingShingle | Iman Akour Nour Mohamed Said Salloum Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection IEEE Access Attention convolutional neural networks credit card fraud detection long-short term memory |
| title | Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection |
| title_full | Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection |
| title_fullStr | Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection |
| title_full_unstemmed | Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection |
| title_short | Hybrid CNN-LSTM With Attention Mechanism for Robust Credit Card Fraud Detection |
| title_sort | hybrid cnn lstm with attention mechanism for robust credit card fraud detection |
| topic | Attention convolutional neural networks credit card fraud detection long-short term memory |
| url | https://ieeexplore.ieee.org/document/11050364/ |
| work_keys_str_mv | AT imanakour hybridcnnlstmwithattentionmechanismforrobustcreditcardfrauddetection AT nourmohamed hybridcnnlstmwithattentionmechanismforrobustcreditcardfrauddetection AT saidsalloum hybridcnnlstmwithattentionmechanismforrobustcreditcardfrauddetection |