cimes-isi / Deceiving-DNN-based-Binary-Matching

Deceiving Deep Neural Networks-Based Binary Code Matching with Adversarial Programs

Artifact for the submission of Deceiving Deep Neural Networks-based Binary Code Matching with Adversarial Programs

Implementation details

Please check here

System requirement

For Uroboros and basic components:

• Ubuntu 18.04 LTS or Windows 10 with WSL and Ubuntu 18.04 for Windows WSL installed

• python3.7 or above installed

• radare2 with r2pipe installed in python

For binaryAI:

• IDA Pro with idapython and decompiler support installed.

For NCC's inst2vec re-training (optional):

• GPU with atleast >8GB for inst2vec model retraining and augmentation

Installing requirements for Uroboros

sudo apt-get update
sudo apt-get -y install gcc gperf bison libtool gcc-multilib python python-dev python-pip gawk build-essential libc6-i386 lib32z1 lib32ncurses5 lib32bz2-1.0 libbz2-1.0:i386 wget git tar gcc-4.8 gcc-4.8-multilib

Installing requirements for attacking ncc

• Use the requirements.txt from Neural Code Comprehension: A Learnable Representation of Code Semantics's repository (here) to install the python dependencies

Installing requirements for attacking binaryAI

• Use the requirements.txt of binaryAI (here) to install the python dependencies
• Make sure you have installed IDA Pro version > 7.1 for the support of IDA Pro microcode API
• Obtain the access key of binaryAI from here
• And paste it to the inside the ida_binaryai_linux.py

Evaluation steps :

1. Create a folder called uroboro_testing
2. Build coreutils and copy to uroboro_testing

Option

The framework will take in 2 argument

1. <seed_program> : The name of the binary under the uroboro_testing folder
2. <function_name> : Name of the function obtained from Objdump/IDA Pro

Running attack for ncc

python3 ./ncc_harness.py -binary_seed <seed_program> -function_name <function_name>

Running attack for asm2vec

python3 ./harness.py -binary_seed <seed_program> -function_name <function_name>

Running attack for binaryAI

python3 ./ida_harness.py -binary_seed <seed_program> -function_name <function_name>

You can see the mutated folders under ./uroboro_testing/function_container_<function_name>_<seed_program>. The presents of bypassed.txt inside the folder indicate a successful attack.