BinEnhance: An Enhancement Framework Based on External Environment Semantics for Binary Code Search

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 analogous external environment for homologous functions by using different inter-function semantic relations (e.g., call, location, data-co-use). After the construction of EESG, we utilize the embeddings generated by existing internal code semantic models to initialize nodes of EESG. Finally, we design a Semantic Enhancement Model (SEM) that uses Relational Graph Convolutional Networks (RGCNs) and a residual block to learn valuable external semantics on the EESG for generating the enhanced semantics embedding. In addition, BinEnhance utilizes data feature similarity to refine the cosine similarity of semantic embeddings. We conduct experiments under six different tasks (e.g., under function inlining scenario) and the results illustrate the performance and robustness of BinEnhance. The application of BinEnhance to HermesSim, Asm2vec, TREX, Gemini, and Asteria on two public datasets results in an improvement of Mean Average Precision (MAP) from 53.6% to 69.7%. Moreover, the efficiency increases fourfold.