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.
翻译:本文探讨了源代码混淆的新方法,即通过在加密生成和关键生成中应用基于文本的经常性神经网络编码器-解码器模型,将序列到序列模型纳入模型结构,以生成模糊代码,生成脱钩键和现场执行。与现有混淆方法进行的数量基准比较表明,对拟议解决方案的偷盗和执行成本有了显著改善,关于模型特性的实验在特性变化、与原始代码基的差异性以及不一致代码的长度方面产生了积极结果。