Adaptive Machine Learning Models for Securing Payment Gateways: A Resilient Approach to Mitigating Evolving Cyber Threats in Digital Transactions

Abstract

Protecting payment gateways from emerging cyber-attacks requires strong and resilient machine learning models. This research examines the efficacy of ensemble techniques like Random Forest, Gradient Boosting, and reinforcement learning to identify suspicious transactions and prevent advanced cyber-attacks. Through transactional data, past cyber threat patterns, and simulated attack patterns, the models proved adaptable in real-time and highly accurate in identifying novel and unknown patterns of attacks. Issues like excessive false positive rates and overfitting were resolved using fine-tuning, feature selection, and cost-sensitive learning. Reinforcement learning also contributed to the system's robustness by allowing repeated learning from feedback and enhancing the ability to detect threats over time. Precision, recall, F1-score, and receiver operating characteristic-area under the curve (ROC-AUC) were used to measure the models' performance, which showed considerable real-time threat detection improvements. This adaptive strategy identifies the promise of self-improving systems to protect digital financial transactions securely. The results emphasize the need to incorporate adaptive machine learning methods into cybersecurity measures to counter the increasing complexity of cyber-attacks.