Blockchain-Based Computational Intelligence Models for Securing Credit Card Transactions in Cyber Forensic

This paper explored how blockchain technology and computational intelligence (CI) models might work together to secure credit card transactions. It focused specifically on real-time fraud detection and prevention. In this work, blockchain, a decentralized, impossible-to-tamper data ledger that is also distributed was used for data integrity and tampering prevention. At the same time, Computational Intelligence models were employed to monitor transactions in real-time to detect anomalies or fraudulent activities. The framework was evaluated with the following key performance indicators: accuracy, precision and recall and F1-score. The results show that when compared to traditional methods, fraud detection rates dramatically improved. A comparative study was conducted across various CI models, including machine learning algorithms and neural networks. As a result, the combination of blockchain and CI models achieved added security and safer credit card transactions. It was found that the scalability of blockchain technology and the computational power necessary to connect with CI models pose some challenges. However, the combined framework proves to be better at preventing fraud than the prevailing method of internet policing. This study will add to an increasingly rich corpus of knowledge on how secured electronic transactions are achieved using modern cryptographic and AI techniques. The government will also benefit from this work in fostering real-time security.