| dc.description.abstract | As malicious attack techniques continue to evolve, in the face of these endless new malicious programs, the problem of sample imbalance often occurs in the dataset, making the classifier unable to learn certain categories of its potential malicious features through sufficient data during the training process. In this study, will apply the Generative Adversarial Networks(GAN), which is a kind of deep learning architecture that trains and generates data for images, to the field of Android malware analysis. GAN has been widely used as data augmentation for other machine vision image recognition researching. In this paper, the characteristics of Android programs are converted into graphical expressions, and a few malicious families are generated by this method to balance and expand the original data set. At the same time, this study also compared other traditional data amplification techniques to explore whether it is beneficial to identify a small number of malicious category samples. Tests have confirmed that both traditional image amplification methods and GAN can improve the accuracy of classification, but the GAN can more effectively improve the classification model. The detection accuracy of the data set was originally low due to the small number of data. The malicious family, the experimental results show that in the different data sets of Drebin′s 4,000 and AMD′s 20,000 samples, the accuracy of the two types of samples with a relatively small number of samples is amplified by the generation of the anti-network, compared to before the amplification. The difference can reach 5%~20%. | en_US |