robust-adv-malware-detection

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robust-adv-malware-detection

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[IEEE S&P Workshop 2018] "Adversarial Deep Learning for Robust Detection of Binary Encoded Malware" Abdullah Al-Dujaili, Alex Huang, Erik Hemberg, Una-May O’Reilly

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# robust-adv-malware-detection
Code repository for the paper [Adversarial Deep Learning for Robust Detection of Binary Encoded Malware](https://arxiv.org/pdf/1801.02950.pdf), A. Al-Dujaili *et al.*, 2018

- Visualization tool in the paper [On Visual Hallmarks of Robustness to Adversarial Malware](https://arxiv.org/pdf/1805.03553.pdf) can be found [here](https://github.com/ALFA-group/adv-malware-viz).

- A series of related blog posts can be found [here](http://ash-aldujaili.github.io/blog/2018/08/29/evasive-malware/).

- Dataset can be shared upon request, please fill in the form https://goo.gl/forms/hn5Dfiset1Y1BkMr1 and we will send you a link to the dataset.

## Installation

All the required packages are specified in the yml files under `helper_files`. If you have `conda` installed, you can just `cd` to the main directory and execute the following with `osx_environment.yml` or `linux_environment.yml` on OSx or Linux, respectively.

```
conda env create —f ./helper_files/(osx|linux)_environment.yml
```
This will create an environment called `nn_mal`.

To activate this environment, execute:
```
source activate nn_mal
```

**Note**: If you're going to use losswise, you may run into an issue of one print line whose argument is not enclosed by brackets,
just put the brackets if this error shows up and you're good to go.

**Note**: If you’re running the code on Mac OS with Cuda, then according to Pytorch.org
“macOS Binaries dont support CUDA, install from source if CUDA is needed”

## Running:

1. Configure your experiment as desired by modifying the `parameters.ini` file. Among the things you may want to to specify:
    a - dataset filepath
    b - gpu device if any
    c - name of the experiment 
    d - training method (inner maximizer)
    e - evasion method

**Note** In case you do not have access to the dataset, you can still run the code on a synthetic dataset with 8-dimensional binary feature vectors, whose bits are set with probability 0.2 for malicious class and 0.8 for benign class.

2. Execute `framework.py`

```
python framework.py
```

**Note**: the experiments can be all logged and monitored using losswise. 
To activate logging, set `losswise_api_key` to your API key in `parameters.in`
and set `is_losswise` to **True**

## Reproducing Paper Results:

In order to reproduce the results in the paper, set the filepaths to the malicious and benign saved feature vectors (these can be re-generated with `generate_vectors.py`) and execute the `run_experiments.py` script

```
python run_experiments.py
```

Results (accuracy metrics, bscn measures,  and evasion rates) will be populated under (to-be-generated) `result_files` directory. On the other hand, the trained models will be saved under `helper_files`.

The results can be compiled into LaTeX tables saved under `result_files` by runnig the function `create_tex_tables()` with the valid filepath to the result files under `utils/script_functions.py`. By default, you can do the following
```
cd utils/
python script_functions.py
```

**NOTE** For linux OS, you may run into the trouble of running `source` from within Python `os.system()`. A workaround is to replace the `os.system()` command in `run_experiments.py` with the following line:
```
system('/bin/bash -c "source activate nn_mal;python framework.py”')
```

## Citation

If you make use of this code and you'd like to cite us, please consider the following:

```
@article{al2018adversarial,
  title={Adversarial Deep Learning for Robust Detection of Binary Encoded Malware},
  author={Al-Dujaili, Abdullah and Huang, Alex and Hemberg, Erik and O'Reilly, Una-May},
  journal={arXiv preprint arXiv:1801.02950},
  year={2018}
}
```

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