By Nedim Srndic and Pavel Laskov, University of Tuebingen.

Homepage: https://github.com/srndic/mimicus

Mimicus was used as the experimental platform for the paper:

Nedim Srndic and Pavel Laskov. Practical Evasion of a Learning-Based Classifier: A Case Study. IEEE Symposium on Security and Privacy, 2014 (PDF).

Mimicus consists of a reusable Python library (in the directory mimicus/) and code for the reproduction of experiments described in the paper (reproduction/).

Mimicus was developed in Python 2.7. Only the library files (the mimicus/ directory) are installed, code and data required for experiment reproduction (the reproduction/ directory) are not installed.

Before proceeding, please make sure you have a recent version of setuptools (>= 3.1):

pip install --upgrade setuptools

You can install Mimicus directly from its Git repository:

git clone https://github.com/srndic/mimicus.git
cd mimicus
python setup.py develop --user

This will install the Mimicus library for the current user and does not require administrative privileges. It will just create a link in the user's site-packages directory, usually ~/.local/lib/python2.7/site-packages, to the Mimicus directory. That way, any modifications you make to Mimicus code will be immediately visible to any code importing Mimicus, so there is no need for reinstallation. Furthermore, because the code remains in the local git repository, you can easily contribute your great new features and bugfixes. Omit "--user" to install system-wide.

Alternatively, you can create a Python egg file:

python setup.py bdist_egg

and install it for your user:

easy_install --user dist/mimicus-*.egg

Omit "--user" to install system-wide.

To uninstall Mimicus, type:

python setup.py develop --uninstall --user

Omit "--user" to uninstall a system-wide installation.

Mimicus requires the curl and perl executables to be installed:

apt-get install curl perl

The following third-party Python libraries are required:

• matplotlib >= 1.1.1rc
• numpy >= 1.6.1
• scikit_learn >= 0.14.1
• scipy >= 0.9.0

They will be automatically installed by setuptools or you can install them manually using pip:

pip install matplotlib
pip install numpy
pip install scikit_learn

There might be problems with scipy installation if you do not already have BLAS installed. You can install scipy by following these directions.

Mimicus provides two different implementations of the Random Forest classifier:

1. R_randomForest, using the randomForest package for the R programming language, and
2. the RandomForestClassifier class of scikit_learn.

If you wish to use the former, please install R, its randomForest package and the rpy2 Python library. Otherwise, the scikit_learn implementation will be used as a fallback. The R version is maintained because it is the one used by PDFrate. The mimicus.classifiers.RandomForest module decides during runtime which implementation to use, depending on whether you have the R implementation installed or not.

Before submitting files to PDFrate, please read the policies.

In order to respect the PDFrate policies and minimize the number of submissions, submissions are scheduled to run periodically and individually, and PDFrate's replies are cached.

New submissions are stored as JSON files in a query directory. The script mimicus/bin/pdfratequeryscheduler.py runs periodically and submits the query with the highest priority or, if there are multiple, the oldest one. The script will then query PDFrate to check any pending queries and save the reply, if it is ready, into the replies directory. The reply remains in the replies directory and is subsequently returned every time a script submits the same file to PDFrate, i.e., there is no danger of multiple submission.

In order for this to work, please schedule the submission script to run in regular time intervals (e.g, using cron) and set up the query and reply directories in the Mimicus configuration file (see Configuration Files).

If you wish to reproduce the experiments described in the paper, you will find that everything is included in this project except the malicious attack candidate files.

Files from the Contagio dataset were used in the experiments described in the paper and we cannot distribute them. They are available here.

The attack files comprise the dataset called Attack. A full list of files in the Attack dataset can be found in data/attack.list. They can be found under the same names in the Contagio repositories.

If you wish to run the attacks using a different set of malicious attack candidate files, you can replace the attack.list file with your own list.

Experiments can be reproduced by running these scripts in the reproduction/ directory, one per attack scenario:

python reproduction/F.py
python reproduction/FC.py
python reproduction/FT.py
python reproduction/FTC.py

Before submitting files to PDFrate, please read the policies.

You can submit a directory of PDF files or PDF files listed in a text file using the reproduction/pdfrate_submit.py script, e.g.:

python reproduction/pdfrate_submit.py results/F_mimicry

To print submission results when they are ready, use the reproduction/pdfrate_report.py script, e.g.:

python reproduction/pdfrate_report.py results/F_mimicry

See Setting up PDFrate Submissions if you haven't already configured PDFrate submissions.

There are two configuration files in this project: one for the Mimicus library and the other for the reproduction code. Both files use the same INI-file-like syntax.

After the installation or the first time you run an attack, the directory $XDG_CONFIG_HOME/mimicus, e.g., ~/.config/mimicus, will be created with the configuration file mimicus.conf inside. Use it to customize your library installation. Options are described in the mimicus/default.conf file.

The first time you run an attack, the configuration file reproduction/custom.conf will be created. Use it to customize the execution of experiments. Options are described in the reproduction/default.conf file.

• mimicus/ - Python package mimicus (library)
  • attacks/ - attack method implementations
  • bin/ - scripts
  • classifiers/ - classifier implementations
  • data/ - data files required for testing the library
  • test/ - code for testing the library
  • tools/ - code for feature extraction, etc.
• results - attack results will be saved in this directory
• reproduction/ - Python code for experiment reproduction
• data/ - data files required to reproduce the experiments
• COPYING - software license
• MANIFEST.in - Python setuptools configuration
• README - this file

Mimicus is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.