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PBDiff: Neural network based program-wide diffing method for binaries.

Lu Yu1,2, Yuliang Lu1,2, Yi Shen1,2

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Summary
This summary is machine-generated.

This study introduces PBDiff, a novel neural network-based method for program-wide binary code diffing. PBDiff enhances accuracy and efficiency in comparing software across different versions and architectures, improving vulnerability detection.

Keywords:
IoT securitybinary diffingfeature extractionneural networkvulnerability detection

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Program-wide binary code diffing is crucial for vulnerability detection.
  • Existing tools like BinDiff and TurboDiff exhibit limitations in accuracy due to variations in software versions, optimization levels, and architectures.

Purpose of the Study:

  • To propose a novel program-wide binary diffing method using neural networks.
  • To achieve accurate and efficient binary code comparison across diverse software versions, optimization levels, and architectures.

Main Methods:

  • A hybrid approach combining top-down (call graph analysis) and bottom-up (neural network-based semantic feature vectorization) processes.
  • Analysis of target files at four granularities.
  • Implementation of a prototype tool named PBDiff.

Main Results:

  • PBDiff demonstrates superior performance compared to state-of-the-art tools like BinDiff, Asm2vec, and TurboDiff.
  • The method effectively handles variations in versions, optimization levels, and architectures.
  • Successful application in diffing and vulnerability detection on real-world firmware files.

Conclusions:

  • The proposed neural network-based binary diffing method significantly improves accuracy and efficiency.
  • PBDiff offers a robust solution for binary analysis tasks, including vulnerability detection, across diverse software contexts.