中圖分類號： TP391
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2022.02.009
引用格式： 林琴萍，李庚，崔潤邦，等. 信貸自動審批模型的對抗攻擊風險研究[J].信息技術(shù)與網(wǎng)絡安全，2022，41(2)：53-60.

Research on adversarial attack risk of automatic credit approval model

Abstract： Recently, many banks and other financial institutions have introduced automated credit approval systems to replace the traditional manual approval. Automatic credit approval system is vulnerable to attack from adversarial examples. This paper presents an experiment of credit adversarial examples attack.Firstly, the XGBoost model is trained based on all applicants′ credit data to predict applicant behavior and select the original sample. Secondly, the ′Non-default applicant′ is used to train the improved GAN model and generate eigenvalues, and then some features of the original sample are modified to construct the confrontation sample. The modified eigenvalue is close to the eigenvalue of the dense distribution of ′Non-default applicant′. Finally, the trained XGBoost model is used to classify the confrontation examples. We find that in the experiment, the generated confrontation examples can confuse the XGBoost model. When the number of modified eigenvalues increases, the generation rate of confrontation examples is on the rise. In short, the experiment shows that the attack from adversarial examples will pose security risks to the automatic credit approval system.

Key words : credit loan;adversarial attack;adversarial examples；generative adversarial networks