FRAUD-RLA: A new reinforcement learning adversarial attack against credit card fraud detection
This addresses a gap in adversarial attack research for credit card fraud detection, though it appears incremental as it builds on existing reinforcement learning methods in a specific domain.
The paper tackles the problem of adversarial attacks on credit card fraud detection systems by proposing FRAUD-RLA, a reinforcement learning-based attack that bypasses classifiers with less required knowledge, showing effectiveness across three datasets and two systems.
Adversarial attacks pose a significant threat to data-driven systems, and researchers have spent considerable resources studying them. Despite its economic relevance, this trend largely overlooked the issue of credit card fraud detection. To address this gap, we propose a new threat model that demonstrates the limitations of existing attacks and highlights the necessity to investigate new approaches. We then design a new adversarial attack for credit card fraud detection, employing reinforcement learning to bypass classifiers. This attack, called FRAUD-RLA, is designed to maximize the attacker's reward by optimizing the exploration-exploitation tradeoff and working with significantly less required knowledge than competitors. Our experiments, conducted on three different heterogeneous datasets and against two fraud detection systems, indicate that FRAUD-RLA is effective, even considering the severe limitations imposed by our threat model.