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Detecting Control Flow Similarities Using Machine Learning Techniques

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1252))

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Abstract

In this work, methods are presented that allow a comparison between control flow paths. The intended use cases for these methods are weak points and bug detection. In existing work, control flow graphs have always been compared with each other to achieve those goals. Nevertheless, vulnerabilities or bugs can be hidden in completely different contexts, i.e. in different parts of the program. Therefore, this work deals with the extraction, coding and comparison of control flow paths. This is because the path of a vulnerability or bug in which the instructions are executed is always similar.

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Notes

  1. 1.

    https://github.com/libgdx/libgdx/tree/master/extensions/gdx-jnigen.

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Author information

Authors and Affiliations

  1. Friedrich-Schiller-University, 07743, Jena, Germany

    André Schäfer & Wolfram Amme

Authors
  1. André Schäfer
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  2. Wolfram Amme
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Corresponding author

Correspondence to André Schäfer .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Schäfer, A., Amme, W. (2021). Detecting Control Flow Similarities Using Machine Learning Techniques. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_48

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