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Intelligent Computing pp 637–647Cite as

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

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 284)

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.

Keywords

  • Code obfuscation
  • Encoder-decoder models

S. Datta—Work performed at the Hong Kong University of Science and Technology.

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  • DOI: 10.1007/978-3-030-80126-7_45
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Notes

  1. 1.

    Code repository: https://github.com/dattasiddhartha/DeepObfusCode.

  2. 2.

    Dataset: https://github.com/dattasiddhartha-1/obfuscation-dataset.

References

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Author information

Authors and Affiliations

  1. University of Oxford, Oxford, UK

    Siddhartha Datta

Authors
  1. Siddhartha Datta
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Correspondence to Siddhartha Datta .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

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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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