Cyber Security Threats Prediction for Credit Card Transactions Using Machine Learning Algorithms

Cybersecurity threats come in various forms, including the compromise of computer security, services, or data, as well as financial crimes such as illegal transactions through banking systems and credit card fraud. This paper introduces essential strategies for identifying financial fraud, a prevalent and costly issue in today's digital and interconnected world. With most transactions now being conducted via credit cards, the risk of fraud has significantly increased in recent times. The vast amount of data generated by digital transactions presents a challenge, rendering manual detection methods both tedious and ineffective. Machine Learning (ML) algorithms offer a solution by employing diverse techniques to accurately identify fraudulent activities. The objective of this research is to highlight the use of proper model selection using the various machine learning algorithms such as logistic regression, support vector machine, k-nearest neighbors, decision trees, and random forest algorithm which is an ensemble learning method derived from decision tree-based approaches, as an efficient tool for credit card fraud detection. Its ability to manage large datasets, navigate non-linear relationships, and perform feature analysis makes Random Forest particularly suitable for addressing this issue.