ENHANCING FRAUD DETECTION SYSTEMS AGAINST ADVERSARIAL ATTACKS USING MACHINE LEARNING

Fraud detection systems play a crucial role in maintaining the integrity and security of financial transactions and various operational processes. However, these systems are increasingly vulnerable to adversarial attacks, which can undermine their effectiveness. This paper explores methods to enhance the robustness of fraud detection systems against such attacks. We introduce novel adversarial attack models, propose advanced adversarial training techniques, and develop real-time detection and prevention mechanisms. The proposed methods are evaluated across multiple domains, including financial transactions, cybersecurity, and customs, demonstrating significant improvements in system resilience and accuracy.