gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation

This repository contains the official implementation and dataset for the paper:

📄 paper

gMBA: Expression Semantic Guided Mixed Boolean-Arithmetic Deobfuscation Using Transformer Architectures
[Accepted at Findings of ACL2025]

🔍 Overview

Mixed Boolean-Arithmetic (MBA) expressions are widely used in software obfuscation to hinder reverse engineering and malware analysis.
gMBA presents a novel Transformer-based sequence-to-sequence model that deciphers obfuscated MBA expressions into their simplified forms.

Unlike prior approaches, gMBA leverages:

• Semantic features from the expression via automatically constructed truth tables
• A lightweight yet expressive Transformer architecture guided by these semantics

⁉️ How to use

git clone https://github.com/your-username/gMBA.git
cd gMBA
pip install -r requirements.txt

You may need to install torch series by the commands below

pip install torch==2.0.0+cu117 -f  https://download.pytorch.org/whl/torch_stable.html

pip install torchtext==0.6.0 -f https://download.pytorch.org/whl/torch_stable.html

pip install torchdata==0.6.0 -f https://download.pytorch.org/whl/torch_stable.html

Then just run your notebook files!

📂 Project Structure

.
├── data/
│   ├── train_s.csv
│   ├── train_s-bool_tt_added.csv
│   ├── train_s-arith_tt_added.csv
│   ├── train_s-both_tt_added.csv
│   ├── test.csv
│   ├── test-bool_tt_added.csv
│   ├── test-arith_tt_added.csv
│   └── test-both_tt_added.csv
│
├── baseline/
│   ├── transformer_baseline.py
│   └── ...
│
├── addition/
│   ├── transformer_add.ipynb
│   └── ...
├── concat_token/
│   ├── transformer_cat_tok.ipynb
│   └── ...
├── concat_hiddim/
│   ├── transformer_cat_hiddim.ipynb
│   └── ...
│
├── README.md
└── requirements.txt