DeepObfusCode: Source Code Obfuscation Through Sequence-to-Sequence Networks
This addresses software protection against reverse engineering, though it appears incremental as it builds on existing neural network approaches.
The paper tackled source code obfuscation by using sequence-to-sequence neural networks to generate obfuscated code and deobfuscation keys, resulting in significant improvements in stealth and execution cost compared to existing methods.
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.