Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models

The security of credit card fraud detection (CCFD) models based on machine learning is important but rarely considered in the existing research. To this end, we propose a black-box attack-based security evaluation framework for CCFD models. Under this framework, the semisupervised learning technique and transfer-based black-box attack are combined to construct two versions of a semisupervised transfer black-box attack algorithm. Moreover, we introduce a new nonlinear optimization model to generate the adversarial examples against CCFD models and a security evaluation index to quantitatively evaluate the security of them. Computing experiments on two real data sets demonstrate that, facing the adversarial examples generated by the proposed attack algorithms, all six supervised models considered largely lose their ability to identify the fraudulent transactions, whereas the two unsupervised models are less affected. This indicates that the CCFD models based on supervised machine learning may possess substantial security risks. In addition, the evaluation results for the security of the models generate important managerial implications that help banks reasonably evaluate and enhance the model security. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 72171160 and 71988101], Key Program of National Natural Science Foundation of China and Quebec Research Foundation (NSFC-FRQ) Joint Project [Grant 7191101304], Key Program of NSFC-FRQSC Joint Project [Grant 72061127002], Excellent Youth Foundation of Sichuan Province [Grant 2020JDJQ0021], and National Leading Talent Cultivation Project of Sichuan University [Grant SKSYL2021-03]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1297 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0076 ) at ( http://dx.doi.org/10.5281/zenodo.7631457 ).