DeepObfusCode: Source Code Obfuscation Through Sequence-to-Sequence Networks

TL;DR

This paper introduces DeepObfusCode, a novel source code obfuscation method using sequence-to-sequence neural networks, achieving improved stealth and efficiency over existing techniques.

Contribution

It presents a new neural network-based approach for code obfuscation that enhances stealth and reduces execution costs compared to prior methods.

Findings

Significant improvement in stealth and execution cost.

Obfuscated code shows high dissimilarity to original code.

Consistent length of obfuscated code.

Abstract

The paper explores a novel methodology in source code obfuscation through the application of text-based recurrent neural network (RNN) encoder-decoder models in ciphertext generation and key generation. Sequence-to-sequence models are incorporated into the model architecture to generate obfuscated code, generate the deobfuscation key, and live execution. Quantitative benchmark comparison to existing obfuscation methods indicate significant improvement in stealth and execution cost for the proposed solution, and experiments regarding the model's properties yield positive results regarding its character variation, dissimilarity to the original codebase, and consistent length of obfuscated code.