Malware Bypass Research using Reinforcement Learning

This is a malware manipulation environment using OpenAI's gym environments. The core idea is based on paper "Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning" (paper). I am extending the original repo because:

1. It is no longer maintained
2. It uses Python2 and an outdated version of LIEF
3. I wanted to integrate new Malware gym environments and additional manipulations

Over the past three years there have been breakthrough open-source projects published in the security ML space. In particular, Ember (Endgame Malware BEnchmark for Research) and MalConv: Malware detection by eating a whole exe (paper) have provided security researchers the ability to develop sophisticated, reproducible models that emulate features/techniques found in NGAVs.

MalwareRL exposes gym environments for both Ember and MalConv to allow researchers to develop Reinforcement Learning agents to bypass Malware Classifiers. Actions include a variety of non-breaking (e.g. binaries will still execute) modifications to the PE header, sections, imports and overlay and are listed below.

ACTION_TABLE = {
    'modify_machine_type': 'modify_machine_type',
    'pad_overlay': 'pad_overlay',
    'append_benign_data_overlay': 'append_benign_data_overlay',
    'append_benign_binary_overlay': 'append_benign_binary_overlay',
    'add_bytes_to_section_cave': 'add_bytes_to_section_cave',
    'add_section_strings': 'add_section_strings',
    'add_section_benign_data': 'add_section_benign_data',
    'add_strings_to_overlay': 'add_strings_to_overlay',
    'add_imports': 'add_imports',
    'rename_section': 'rename_section',
    'remove_debug': 'remove_debug',
    'modify_optional_header': 'modify_optional_header',
    'modify_timestamp': 'modify_timestamp',
    'break_optional_header_checksum': 'break_optional_header_checksum',
    'upx_unpack': 'upx_unpack',
    'upx_pack': 'upx_pack'
}

The observation_space of the gym environments are an array representing the feature vector. For ember this is numpy.array == 2381 and malconv numpy.array == 1024**2. The MalConv gym presents an opportunity to try RL techniques to generalize learning across large State Spaces.

A baseline agent RandomAgent is provided to demonstrate how to interact w/ gym environments and expected output. This agent attempts to evade the classifier by randomly selecting an action. This process is repeated up to the length of a game (e.g. 50 mods). If the modifed binary scores below the classifier threshold we register it as an evasion. In a lot of ways the RandomAgent acts as a fuzzer trying a bunch of actions with no regard to minimizing the modifications of the resulting binary.

Additional agents will be developed and made available (both model and code) in the coming weeks.

Table 1: Evasion Rate against Ember Holdout Dataset*

* 250 random samples

To get malware_rl up and running you will need the follow external dependencies:

• LIEF
• Ember and Malconv models. Both of these then need to be placed into the malware_rl/envs/utils/ directory.
• UPX has been added to support pack/unpack modifications. Download the binary here and place in the malware_rl/envs/controls directory.
• Benign binaries - a small set of "trusted" binaries (e.g. grabbed from base Windows installation) you can download some via MSFT website (example). Store these binaries in malware_rl/envs/controls/trusted
• Run strings command on those binaries and save the output as .txt files in malware_rl/envs/controls/good_strings
• Download a set of malware from VirusShare or VirusTotal. I just used a list of hashes from the Ember dataset

I used a conda env set for Python3.7:

conda create -n malware_rl python=3.7

Finally install the Python3 dependencies in the requirements.txt.

pip3 install -r requirements.txt

The are a bunch of good papers/blog posts on manipulating binaries to evade ML classifiers. I compiled a few that inspired portions of this project below. Also, I have inevitably left out other pertinent reseach, so if there is something that should be in here let me know in an Git Issue or hit me up on Twitter (@filar).

• Demetrio, Luca, et al. "Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations." arXiv preprint arXiv:2003.13526 (2020). (paper)
• Demetrio, Luca, et al. "Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection." arXiv preprint arXiv:2008.07125 (2020). (paper)
• Song, Wei, et al. "Automatic Generation of Adversarial Examples for Interpreting Malware Classifiers." arXiv preprint arXiv:2003.03100 (2020). (paper)
• Suciu, Octavian, Scott E. Coull, and Jeffrey Johns. "Exploring adversarial examples in malware detection." 2019 IEEE Security and Privacy Workshops (SPW). IEEE, 2019. (paper)
• Fleshman, William, et al. "Static malware detection & subterfuge: Quantifying the robustness of machine learning and current anti-virus." 2018 13th International Conference on Malicious and Unwanted Software (MALWARE). IEEE, 2018. (paper)
• Pierazzi, Fabio, et al. "Intriguing properties of adversarial ML attacks in the problem space." 2020 IEEE Symposium on Security and Privacy (SP). IEEE, 2020. (paper/code)
• Fang, Zhiyang, et al. "Evading anti-malware engines with deep reinforcement learning." IEEE Access 7 (2019): 48867-48879. (paper)

• Evading Machine Learning Malware Classifiers: for fun and profit!
• Cylance, I Kill You!
• Machine Learning Security Evasion Competition 2020
• ML evasion contest – the AV tester’s perspective

• 42: The answer to life the universe and everything offensive security by Will Pearce, Nick Landers (slides)
• Bot vs. Bot: Evading Machine Learning Malware Detection by Hyrum Anderson (slides)
• Trying to Make Meterpreter into an Adversarial Example by Andy Applebaum (slides)