Using Generative Adversarial Networks for Data Augmentation in Android Malware Detection

Yi Ming Chen, Chun Hsien Yang, Guo Chung Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations

Abstract

In the field of mobile malware detection, the problem of sample imbalance often occurs in the dataset, making the classifier unable to learn features through sufficient data during the training process. This research used the generative adversarial networks (GAN). In this paper, features of malwares are transformed into image expressions, and data is generated from a small number of malicious families to balance and expand the original dataset. We also compare other data augmentation techniques to explore whether they are beneficial to identify a small number of malicious samples. Experiments show that both traditional techniques and GAN can improve the accuracy of classification, but GAN can more effectively improve the classification model to detect that the dataset originally has a small number of datasets and the recognition accuracy is lower. The experimental results show that in the different datasets of 4,000 data in Drebin and 20,000 data in AMD, the types with a relatively small number of samples are augmented by the GAN. Compared with before and after data augmentation, the difference in F1-score accuracy can reach 5%20%.

Original languageEnglish
Title of host publication2021 IEEE Conference on Dependable and Secure Computing, DSC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728175348
DOIs
StatePublished - 30 Jan 2021
Event2021 IEEE Conference on Dependable and Secure Computing, DSC 2021 - Aizuwakamatsu, Fukushima, Japan
Duration: 30 Jan 20212 Feb 2021

Publication series

Name2021 IEEE Conference on Dependable and Secure Computing, DSC 2021

Conference

Conference2021 IEEE Conference on Dependable and Secure Computing, DSC 2021
Country/TerritoryJapan
CityAizuwakamatsu, Fukushima
Period30/01/212/02/21

Keywords

  • Android
  • Data Augmentation
  • Deep Learning
  • Generative Adversarial Nets

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