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BinEncoder: Binary code similarity detection across compilation configurations via microcode

delete2026-07-29
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PRE
AI
Z
Zheng Zhao
T
Tianhao Zhang
Q
Qian Mao
Q
Qi Zhao
X
Xiangyang Luo *
X
Xiaoya Fan *
DOI:10.1007/s10489-026-07390-zdelete
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Abstract

Abstract

En 中文
Binary Code Similarity Detection (BCSD) plays a critical role in software security. However, accurately measuring the similarity between binaries compiled from the same source code under different compilation configurations remains a major challenge. To address this issue, we propose BinEncoder, a novel BCSD framework that extracts high-level semantics of binary functions through a microcode-based representation. Specifically, BinEncoder lifts binary code into Hex-Rays microcode, an intermediate representation, and decomposes composite instructions into nested instructions to expose fine-grained semantics. It then employs a Transformer model pre-trained on three tailored tasks to capture structural and contextual information. Extensive experiments demonstrate that BinEncoder achieves superior performance over representative BCSD baselines across multiple BCSD tasks and shows promising potential for retrieval-based vulnerability detection.
Keywords:
Binary code similarity detection
Microcode
Vulnerability detection
Deep learning
Compilation configuration

Journal

Applied Intelligence cover
Applied Intelligence
IF:
3.5
Papers:
7.5K
Citations:
1.7W

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College of Light Industry
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School of Software Technology
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Faculty of Information
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information engineering university
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371
Papers: 108
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College of Artificial Intelligence
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Papers: 140
Citations: 1
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