Malware-GAN
This is a realization of "Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN". The reaserch is done in Institute of Automation, Chinese Academy of Sciences, Summer 2018. http://english.ia.cas.cn/ with the help of my mentor XuBo.
Introduction
The idea is to use a generative adversarial network (GAN) based algorithm to generate adversarial malware examples, which are able to bypass black-box machine learning based detection models.
Dependencies
Tensorflow 1.80 Keras 2.0 Cuckoo Sandbox 2.03
Dataset
The original malware sample is acquired from https://virusshare.com/, I have used 3000 malware samples and 1500 benign samples for trainning and testing(will expand further).
I used the Cuckoo Sandbox to extract API features from samples. https://cuckoo.readthedocs.io/en/2.0.3/
128 API features are selcted as dimensional vectors to be input to the neutual network (API_list.txt)
Experiments
MalGAN Architecture:
generator：128+20—256—128
subsitite detector：128—256—1
maximum epoch：100
True positive rate (in percentage) on original samples and adversarial examples when MalGAN and the black-box detector are trained on the SAME training set
MLP-TPR
True positive rate (in percentage) on original samples and adversarial examples when MalGAN and the black-box detector are trained on the DIFFERENT training set
True positive rate (in percentage) on the adversarial examples after the black-box detector is retrained (RF for example)
