https://github.com/yanminglai/Malware-GAN

Realization of paper: "Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN" 2017
https://github.com/yanminglai/Malware-GAN

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Realization of paper: "Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN" 2017

• Host: GitHub
• URL: https://github.com/yanminglai/Malware-GAN
• Owner: yanminglai
• License: gpl-3.0
• Created: 2018-07-02T06:39:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-20T07:56:47.000Z (over 6 years ago)
• Last Synced: 2024-08-12T08:09:28.647Z (5 months ago)
• Language: Python
• Homepage: https://arxiv.org/abs/1702.05983
• Size: 2.38 MB
• Stars: 114
• Watchers: 5
• Forks: 57
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-MLSecOps - Malware-GAN

README

        
# 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.

![alt text](https://github.com/yanminglai/Malware-GAN/blob/master/figures/Architecture.png)

## 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

![alt text](https://github.com/yanminglai/Malware-GAN/blob/master/figures/TPR_allclassifiers.png)

MLP-TPR

![alt text](https://github.com/yanminglai/Malware-GAN/blob/master/figures/MLPTPR.png)

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

![alt text](https://github.com/yanminglai/Malware-GAN/blob/master/figures/TPR_DIF.png)

True positive rate (in percentage) on the adversarial examples after the black-box detector is retrained   (RF for example)

![alt text](https://github.com/yanminglai/Malware-GAN/blob/master/figures/retained.png)