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.
S. Datta—Work performed at the Hong Kong University of Science and Technology.
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Notes
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Code repository: https://github.com/dattasiddhartha/DeepObfusCode.
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Datta, S. (2021). DeepObfusCode: Source Code Obfuscation through Sequence-to-Sequence Networks. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 284. Springer, Cham. https://doi.org/10.1007/978-3-030-80126-7_45
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DOI: https://doi.org/10.1007/978-3-030-80126-7_45
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