• Citer

Fraud-RLA: A Reinforcement Learning Adversarial Attack Against Credit Card Fraud Detection

par Lunghi, Daniele ;Molinghen, Yannick ;Simitsis, Alkis;Lenaerts, Tom ;Bontempi, Gianluca
Référence I E E E Transactions on Dependable and Secure Computing, 23, 3, page (7468-7481)
Publication Publié, 2026-07-01

Article révisé par les pairs

• Accès en ligne
• Détails
• Contenu
• Statistiques

Résumé :

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.