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README.md

ffridataset2019

You can make datasets like FFRI Dataset 2019 by using this script.

Requirements

  • Python 3.6
  • pipenv

Alternatively, you can use Docker. See Using Docker.

Prepare To Use

Attention We recommend that you run the following commands in the working directory (ffridataset2019 directory).

sudo apt install libfuzzy-dev
sudo apt install unar
sudo apt install cmake

pipenv shell
pipenv install

git clone https://github.com/knowmalware/pehash.git
cd pehash
python setup.py install
cd ..

wget https://github.com/trendmicro/tlsh/archive/master.zip -O tlsh-master.zip
unar tlsh-master.zip
cd tlsh-master
./make.sh
cd py_ext
python ./setup.py install
cd ../Testing
./python_test.sh
cd ../..

wget mark0.net/download/trid_linux_64.zip
unar trid_linux_64.zip
cp trid_linux_64/trid ./
wget mark0.net/download/triddefs.zip
unar triddefs.zip

wget https://github.com/K-atc/PEiD/releases/download/v0.1.1/PEiD
./PEiD --prepare
sudo updatedb

Run Tests

Attention Do not store a file named test.exe in the working directory. The test script copies testbin/test.exe in the directory and removes it.

python test_main.py

Make A Data CSV

This script requires a csv file which contains file information such as labels, dates, file paths. For instance,

path,label,date
~/cleanware/a,0,
~/malware/b,1,2018/01/01

How To Use

Attention Do not store malware and cleanware in the working directory. Due to the limitation of trid, the script copies malware and cleanware in the directory and removes them.

python main.py --csv <path/to/csv> --out <path/to/output_dataset_dir> --log <path/to/log_file>

Using Docker

docker build --tag ffridataset2019 .
docker run -v <path/to/here>/testbin:/work/testbin ffridataset2019 python3 test_main.py
# Note that data directory contains a CSV file and executable files which you want to process.
docker run -v <path/to/here>/data:/work/data -v <path/to/here>/out_dir:/work/out_dir ffridataset2019 python3 main.py --csv ./data/target.csv --out ./out_dir --log ./dataset.log

When using Docker, there exist some limitations:

  • file paths in a CSV file should be specified as a relative path to the container's working directory. Example CSV file is as follows.
path,label,date
./data/cleanware/test0.exe,0,2018/01/01
./data/malware/test1.exe,1,2018/01/02
  • You should mount the host directory which contains both a csv file and executable files to the container's /work/data.
  • You should mount the host directory in which you want to output JSON files to the container's /work/out_dir.

Notes about hashes

TESTED

  • Ubuntu 18.04 on WSL on Windows 10 Pro 1803

Author

Yuki Mogi. © FFRI, Inc. 2019

Koh M. Nakagawa. © FFRI, Inc. 2019

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