/MalwareGAN

Adversarial Malware Generator Using GANs

Primary LanguagePythonMIT LicenseMIT

Adversarial Malware Generation Using GANs

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Implementation of a Generative Adversarial Network (GAN) that can create adversarial malware examples. The work is inspired by MalGAN in the paper "Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN" by Weiwei Hu and Ying Tan.

Framework written in PyTorch and supports CUDA.

Running the Script

The malware GAN is provided as a package in the folder malgan. A driver script is provided in main.py, which processes input arguments via argparse. The basic interface is:

python main.py Z BATCH_SIZE NUM_EPOCHS MALWARE_FILE BENIGN_FILE
  • Z -- Dimension of the latent vector. Must be a positive integer.
  • BATCH_SIZE -- Batch size for malicious examples. The benign batch size is proportional to BATCH_SIZE and the fraction of total training samples that are benign.
  • NUM_EPOCHS -- Maximum number of training epochs
  • MALWARE_FILE -- Path to a serialized numpy or torch matrix where the rows represent a single malware file's binary feature vector.
  • BENIGN_FILE -- Path to a serialized numpy or torch matrix where the rows represent a single benign file's binary feature vector.

For checkout purposes, we recommend calling:

python main.py 10 32 100 data/trial_mal.npy data/trial_ben.npy

Dataset

A trial dataset is included with this implementation in the data folder. The data was publish in the repository: yanminglai/Malware-GAN. This dataset should only be used for proof of concept and initial trials.

We recommend the SLEIPNIR dataset. It was published by ad-Dujaili et al. The authors requested that the dataset not be shared publicly, and we respect that request. However, researchers and students may request access directly from the authors as described on their Github repository. Look for the link to the Google form.

CUDA Support

The implementation supports both CPU and CUDA (i.e., GPU) execution. If CUDA is detected on the system, the implementation defaults to CUDA support.

Requirements

This program was tested with Python 3.6.5 on MacOS and on Debian Linux. requirements.txt enumerates the exact packages used. A summary of the key requirements is below:

  • PyTorch (torch) -- Ver. 1.2.0
  • Scikit-Learn (sklearn) -- Ver. 0.20.2
  • NumPy (numpy)
  • TensorboardX -- If runtime profiling is not required, this can be removed.