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BinBench: a benchmark for x64 portable operating system interface binary function representations.

Francesca Console1, Giuseppe D'Aquanno1, Giuseppe Antonio Di Luna1

  • 1Department of Computer, Control and Management Engineering, University of Roma "La Sapienza", Rome, Italy.

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|June 22, 2023
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Summary

This study introduces BinBench, the first multi-task benchmark for evaluating machine learning models on assembly functions. BinBench enables standardized comparison and testing of model generality across various binary analysis tasks.

Keywords:
Assembly languageBenchmarkBinary functionsBinary functions representationBinary similarityCompiler provenanceDatasetNeural networks

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Area of Science:

  • Computer Science
  • Machine Learning
  • Software Engineering

Background:

  • The field of assembly language processing lacks standardized benchmarks for evaluating machine learning models.
  • Deep neural networks, particularly those from Natural Language Processing (NLP), are increasingly applied to assembly code analysis.
  • Comparing different machine learning models on assembly tasks is challenging due to the absence of common evaluation platforms.

Purpose of the Study:

  • To introduce BinBench, the first multi-task benchmark designed for evaluating machine learning models on low-level assembly functions.
  • To facilitate direct comparison of diverse machine learning models and assess their performance across multiple binary analysis tasks.
  • To promote the rigorous evaluation of model generality in the domain of assembly language processing.

Main Methods:

  • Development of BinBench, a novel benchmark comprising a dataset of binary functions and multiple associated analysis tasks.
  • Inclusion of diverse binary analysis tasks within the benchmark suite.
  • Evaluation of baseline machine learning models using the proposed dataset and tasks.

Main Results:

  • Establishment of a standardized framework for assessing machine learning models on assembly code.
  • Demonstration of the benchmark's utility through the evaluation of baseline models.
  • Provision of a publicly available dataset to support reproducible research.

Conclusions:

  • BinBench addresses the critical need for standardized evaluation in assembly language processing.
  • The benchmark enables robust comparison of machine learning models and promotes the development of more generalizable solutions.
  • The availability of BinBench and its dataset will accelerate progress in applying deep learning to binary code analysis.