Cloud-Enabled Convolutional Neural Networks for Fraudulent Credit Card Transaction Identification

Abstract

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With the rapid digitalization of financial systems and the widespread use of online payment platforms, credit card fraud has become a persistent and escalating threat. Addressing these challenges requires a secure, intelligent, and adaptive detection framework. This study proposes a cloudenabled fraud detection architecture that integrates improved Kepler optimization (IKO) with lightweight cryptography to securely store credit card transaction data in the cloud, ensuring confidentiality and resilience against breaches. For fraud detection, a deep multi-kernel learning integrated with convolutional neural network (CNN) to identify anomalous transaction patterns with high generalization capability. The multi-kernel approach enables the model to capture diverse feature representations across spatial and temporal dimensions of the data. The proposed model was validated using a realworld credit card transaction dataset comprising a high volume of both fraudulent and non-fraudulent records. Experimental results demonstrate the superior performance of our model in terms of accuracy, robustness, and detection efficiency, even under data imbalance conditions. The findings confirm that the integration of CNN with optimized feature selection and secure cloud storage presents optimal solution for real-time credit card fraud identification.