BinEnhance: An Enhancement Framework Based on External Environment Semantics for Binary Code Search
Yongpan Wang, Hong Li, Xiaojie Zhu, Siyuan Li, Chaopeng, Dong, Shouguo Yang, Kangyuan Qin

TL;DR
BinEnhance introduces an external environment semantic graph and a semantic enhancement model to improve binary code search accuracy and robustness by leveraging inter-function semantics.
Contribution
The paper proposes BinEnhance, a novel framework that constructs an external environment semantic graph and uses relational graph convolutional networks for semantic enhancement in binary code search.
Findings
Improves MAP from 53.6% to 69.7% across tasks.
Enhances robustness and efficiency fourfold.
Effective in complex scenarios like function inlining.
Abstract
Binary code search plays a crucial role in applications like software reuse detection. Currently, existing models are typically based on either internal code semantics or a combination of function call graphs (CG) and internal code semantics. However, these models have limitations. Internal code semantic models only consider the semantics within the function, ignoring the inter-function semantics, making it difficult to handle situations such as function inlining. The combination of CG and internal code semantics is insufficient for addressing complex real-world scenarios. To address these limitations, we propose BinEnhance, a novel framework designed to leverage the inter-function semantics to enhance the expression of internal code semantics for binary code search. Specifically, BinEnhance constructs an External Environment Semantic Graph (EESG), which establishes a stable and…
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Taxonomy
TopicsService-Oriented Architecture and Web Services · Semantic Web and Ontologies · Web Data Mining and Analysis
