  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.
