Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent activities and adversarial attacks, which threaten their security and trustworthiness. In the last decade or so, the research interest on adversarial machine learning has grown significantly, revealing how learning applications could be severely impacted by effective attacks. Although early results of adversarial machine learning indicate the huge potential of the approach to specific domains such as image processing, still there is a gap in both the research literature and practice regarding how to generalize adversarial techniques in other domains and applications. Fraud detection is a critical defense mechanism for data economy, as it is for other applications as well, which poses several challenges for machine learning. In this work, we describe how attacks against fraud detection systems differ from other applications of adversarial machine learning, and propose a number of interesting directions to bridge this gap.

翻译：数据经济依赖于数据驱动系统，而复杂的机器学习应用正是由这些系统所驱动。然而不幸的是，机器学习模型面临着欺诈活动和对抗性攻击的威胁，这危及了其安全性和可信度。在过去十年左右的时间里，对抗性机器学习的研究兴趣显著增长，揭示了学习应用可能因有效的攻击而受到严重影响。尽管对抗性机器学习的早期结果表明该方法在图像处理等特定领域具有巨大潜力，但在研究文献和实际应用方面，如何将对抗性技术推广到其他领域和应用程序仍然存在差距。欺诈检测是数据经济的关键防御机制，正如其之于其他应用一样，这为机器学习带来了诸多挑战。在这项工作中，我们描述了针对欺诈检测系统的攻击与对抗性机器学习其他应用的不同之处，并提出了若干有趣的研究方向以弥合这一差距。