https://github.com/wapiti08/defense_evasion_with_gan

The project of Ransomware Detection with GAN
https://github.com/wapiti08/defense_evasion_with_gan

adversarial-attacks cuckoo dynamic-programming gan ransomware

Last synced: 6 months ago
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The project of Ransomware Detection with GAN

• Host: GitHub
• URL: https://github.com/wapiti08/defense_evasion_with_gan
• Owner: Wapiti08
• License: mit
• Created: 2020-05-15T01:08:51.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-10-02T12:23:01.000Z (about 1 year ago)
• Last Synced: 2025-04-15T03:46:11.651Z (6 months ago)
• Topics: adversarial-attacks, cuckoo, dynamic-programming, gan, ransomware
• Language: Jupyter Notebook
• Homepage:
• Size: 12.3 MB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

Awesome Lists containing this project

README

          
# Analysis-on-GAN

![Authour](https://img.shields.io/badge/Author-Wapiti08-blue.svg) 

![Python](https://img.shields.io/badge/Python-3.8-brightgreen.svg) 

![GAN](https://img.shields.io/badge/GAN-GAN-redgreen.svg)

![Ransomware](https://img.shields.io/badge/Ransomware-Locky%20Crypto-redgreen.svg)

![License](https://img.shields.io/badge/license-MIT3.0-green.svg)

[![DOI](https://zenodo.org/badge/264062663.svg)](https://doi.org/10.5281/zenodo.13881242)

---

This is my project about Ransome Detection using GAN, the experiment was implemented by cuckoo. 

### Features:

- read report files from specific location (set in cuckoo) and extract features from them

- features engineering on choosing features with highest weights

- generate matrix based on the features(occurences of features)

- experiments with black attack for the GAN model

- experiments based on different parameters, number of hidden layers, number of nodes

- experiments based on ensemble classifiers

- experiments based on ensemble neural networks

- experiments on both ransomware dataset and generous malware

### Pipelines：

    - collect dataset:

        - please put the generated reports seperately under dataset folder

        - run the col_reports inside the processing (specify your own folder)

    

    - generate raw features:

        - run the the feature_gen to generated seperate features

    - get the total unique feature list:

        - run the feature_dict to get features list

    - get the database based on feature occurrences

        - run the feature_fin to get dataset.csv

    - classifier analysis (in classifiers):

       - analyse and filter the generated datasets and classifier algorithms

    - model experiment:

        - test the models with filter features

For the ransomware and cuckoo configuration, please check the link [cuckoo-download-instructions](https://github.com/Wapiti08/cuckoo-download-instructions)