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        <title>Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures</title>
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        <namePart type="given">Junzhe</namePart>
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    <abstract>Binary code analysis is indispensable for a variety of software security tasks. Applying deep learning to binary code analysis has drawn great attention because of its notable performance. Today, source code is frequently compiled for various Instruction Set Architectures (ISAs). It is thus critical to expand binary analysis capabilities to multiple ISAs. Given a binary analysis task, the scale of available data on different ISAs varies. As a result, the rich datasets (e.g., malware) for certain ISAs, such as x86, lead to a disproportionate focus on these ISAs and a negligence of other ISAs, such as PowerPC, which suffer from the “data scarcity” problem. To address the problem, we propose to learn cross-architecture instruction embeddings (CAIE), where semantically-similar instructions, regardless of their ISAs, have close embeddings in a shared space. Consequently, we can transfer a model trained on a data-rich ISA to another ISA with less available data. We consider four ISAs (x86, ARM, MIPS, and PowerPC) and conduct both intrinsic and extrinsic evaluations (including malware detection and function similarity comparison). The results demonstrate the effectiveness of our approach to generate high-quality CAIE with good transferability.</abstract>
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%0 Conference Proceedings
%T Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures
%A Wang, Junzhe
%A Zeng, Qiang
%A Luo, Lannan
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-learning-cross
%X Binary code analysis is indispensable for a variety of software security tasks. Applying deep learning to binary code analysis has drawn great attention because of its notable performance. Today, source code is frequently compiled for various Instruction Set Architectures (ISAs). It is thus critical to expand binary analysis capabilities to multiple ISAs. Given a binary analysis task, the scale of available data on different ISAs varies. As a result, the rich datasets (e.g., malware) for certain ISAs, such as x86, lead to a disproportionate focus on these ISAs and a negligence of other ISAs, such as PowerPC, which suffer from the “data scarcity” problem. To address the problem, we propose to learn cross-architecture instruction embeddings (CAIE), where semantically-similar instructions, regardless of their ISAs, have close embeddings in a shared space. Consequently, we can transfer a model trained on a data-rich ISA to another ISA with less available data. We consider four ISAs (x86, ARM, MIPS, and PowerPC) and conduct both intrinsic and extrinsic evaluations (including malware detection and function similarity comparison). The results demonstrate the effectiveness of our approach to generate high-quality CAIE with good transferability.
%U https://aclanthology.org/2024.findings-naacl.84
%P 1320-1332

Markdown (Informal)

[Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures](https://aclanthology.org/2024.findings-naacl.84) (Wang et al., Findings 2024)

• Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures (Wang et al., Findings 2024)

ACL

• Junzhe Wang, Qiang Zeng, and Lannan Luo. 2024. Learning Cross-Architecture Instruction Embeddings for Binary Code Analysis in Low-Resource Architectures. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1320–1332, Mexico City, Mexico. Association for Computational Linguistics.