TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Resilience against Adversarial Examples: Data-Augmentation Exploiting Generative Adversarial Networks |
| 영문제목(English Title) |
Resilience against Adversarial Examples: Data-Augmentation Exploiting Generative Adversarial Networks |
| 저자(Author) |
Mingu Kang
HyeungKyeom Kim
Suchul Lee
Seokmin Han
|
| 원문수록처(Citation) |
VOL 15 NO. 11 PP. 4105 ~ 4121 (2021. 11) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Recently, malware classification based on Deep Neural Networks (DNN) has gained significant attention due to the rise in popularity of artificial intelligence (AI). DNN-based malware classifiers are a novel solution to combat never-before-seen malware families because this approach is able to classify malwares based on structural characteristics rather than requiring particular signatures like traditional malware classifiers. However, these DNN-based classifiers have been found to lack robustness against malwares that are carefully crafted to evade detection. These specially crafted pieces of malware are referred to as adversarial examples. We consider a clever adversary who has a thorough knowledge of DNN-based malware classifiers and will exploit it to generate a crafty malware to fool DNN-based classifiers. In this paper, we propose a DNN-based malware classifier that becomes resilient to these kinds of attacks by exploiting Generative Adversarial Network (GAN) based data augmentation. The experimental results show that the proposed scheme classifies malware, including AEs, with a false positive rate (FPR) of 3.0% and a balanced accuracy of 70.16%. These are respective 26.1% and 18.5% enhancements when compared to a traditional DNN-based classifier that does not exploit GAN. |
| 키워드(Keyword) |
Malware classification
Microsoft Malware Classification Challenge
conditional GAN
Data augmentation
Adversarial Examples
|
| 파일첨부 |
PDF 다운로드
|
