Credit card fraud detection using moth-flame earth worm optimisation algorithm-based deep belief neural network
by S. Deepika; S. Senthil
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 14, No. 1, 2022

Abstract: Nowadays, credit card fraud actions happen commonly, which results in vast financial losses. Fraudulent transactions can take place in a variety of ways and can be put into various categories. Hence, financial institutions and banks put forward credit card fraud detection applications. To detect fraudulent activities, this paper proposes a credit card fraud detection system. The proposed system uses the database with the credit card transaction information and sends it to the pre-processing. The log transformation is applied over the database for data regulation in the pre-processing step. After, the appropriate features are selected by the information gain criterion, and the selected features are utilised to train the classifier. Here, a novel classifier, namely moth-flame earth worm optimisation-based deep belief network (MF-EWA-based DBN) is proposed for the fraud detection. The weights for the classifier are selected by the newly developed moth-flame earth worm optimisation algorithm (MF-EWA). The proposed classifier carries out the training and detects the fraud transactions in the database. The proposed MF-EWA-based DBN classifier has improved detection performance and outclassed other existing models with 85.89% accuracy.

Online publication date: Tue, 04-Jan-2022

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