Code and Data Repository for Black-Box Attack-Based Security Evaluation Framework for Credit Card Fraud Detection Models
提出一个基于黑盒攻击的信用卡欺诈检测模型安全评估框架，结合半监督学习和迁移攻击构建STBA算法，并引入非线性优化模型生成对抗样本，用于定量评估模型安全性。
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 semi-supervised learning technique and transfer-based black-box attack are combined to construct two versions of semi-supervised transfer black-box attack (STBA) 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. This project contains four folders: data, results, src, scripts. data：include two datasets used in the paper and a toy dataset for debugging. results: include the experimental results. src: include the source code. scripts: include two scripts for evaluating the security of machine learning models based on substitute models LR and SVM.