Fraud-RLA: A Reinforcement Learning Adversarial Attack Against Credit Card Fraud Detection
Abstract
Adversarial attacks pose a significant threat to data-driven systems, and researchers have devoted considerable effort to studying them. Despite its economic relevance, credit card fraud detection has received comparatively little attention. To address this gap, we propose a novel threat model that highlights the limitations of existing attacks and motivates new approaches. We introduce Fraud-RLA, an adversarial attack against credit card Fraud Detection Systems that leverages Reinforcement Learning to evade detection. Fraud-RLA is designed to maximize the amount stolen by optimizing the exploration-exploitation trade-off while requiring substantially less prior knowledge than competing methods. Our experiments on a realistic Fraud Detection System show that Fraud-RLA is effective, even under the severe limitations imposed by our threat model.
Fields of Study & Subjects
Imbalanced Data Classification Techniques
Vehicle License Plate Recognition
Electricity Theft Detection Techniques
Publication details
- Journal / source
- IEEE Transactions on Dependable and Secure Computing
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 23
- Issue
- 3
- Pages
- 7468–7481
- Publication date
- 13 March 2026
- Language
- EN