Adversarial sample generation technology of malicious code based on LIME

Based on the research and analysis of machine learning technology to detect malicious code, a local interpretable model-agnostic explanations (LIME)-based black-box adversarial examples generation method is proposed to generate adversarial samples for any black-box malicious code classifier and bypass the detection of machine learning models. The method uses a simple model to simulate the target classifier's local performances, obtains the feature weights, and generates disturbances through the disturbance algorithm. According to the generated disturbances, the method modifies the original malicious code to generate adversarial samples. We test the method using Microsoft's common malicious sample data in 2015 and the collected benign sample data from more than 50 suppliers as follows: 18 target classifiers based on different algorithms or features were implemented concerning common malicious code classifiers. Their classifiers' true positive rates were reduced to approximately zero when we attacked them using the method. Two advanced black-box sample generation methods, MalGAN and ZOO, were reproduced for comparison with this method. The experimental results show that the proposed method in this paper can effectively generate adversarial samples, and the method itself owns various strengths, including broad applicability, flexible control of disturbances, and soundness. © 2022, Editorial Board of JBUAA. All right reserved.