Wave Hedges distance-based feature fusion and hybrid optimization-enabled deep learning for cyber credit card fraud detection

With the emerging trend in e-commerce, an increasing number of people have adopted cashless payment methods, especially credit cards for buying products online. However, this ever-rising usage of credit cards has also led to an increase in the malicious users attempting to gain financial profits by committing fraudulent activities resulting in huge losses to the card issuer as well as the customer. Credit Card Frauds (CCFs) are pervasive worldwide, and so efficient methods are required to detect CCFs to minimize financial losses. This research presents an efficient CCF Detection (CCFD) approach based on Deep Learning. In this work, CCFD is performed based on the features obtained from the credit card fused based on Wave Hedge distance, and the Wave Hedge coefficient utilized for fusion is estimated using the Deep Neuro-Fuzzy Network. Further, detection is performed using the Zeiler and Fergus Network (ZFNet), whose trainable factors are adjusted using the Dwarf Mongoose-Shuffled Shepherd Political Optimization (DMSSPO) algorithm. Moreover, the DMSSPO_ZFNet is analyzed based on accuracy, sensitivity, and specificity, and the experimental outcomes reveal that the values attained are 0.961, 0.961, and 0.951.