Credit card fraud detection using moth-flame earth worm optimisation algorithm-based deep belief neural network. (17th December 2021)

• Record Type: Journal Article
• Authors: Deepika, S.; Senthil, S.
• Is Part Of: International journal of electronic security and digital forensics. Volume 14:Number 1(2022)
• Page Start: 53
• Page End: 75
• Publication Date: 2021-12-17
• Subjects: credit card transactions -- fraud detection -- information gain -- earthworm optimisation algorithm -- deep belief network; Computer crimes -- Periodicals; Computer crimes -- Investigation -- Periodicals; Computer security -- Periodicals; Electronic commerce -- Law and legislation -- Periodicals; Information warfare -- Periodicals; 005.805
• Languages: English
• ISSNs: 1751-911X

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.