Session 11B: Binary Analysis  Authors, Creators & Presenters: Yongpan Wang (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Hong Li (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Xiaojie Zhu (King Abdullah University of Science and Technology, Thuwal, Saudi Arabia), Siyuan Li (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Chaopeng Dong (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China), Shouguo Yang (Zhongguancun Laboratory, Beijing, China), Kangyuan Qin (Institute of Information Engineering Chinese Academy of Sciences & University of Chinese Academy of Sciences, China) PAPER BinEnhance: An Enhancement Framework Based on External Environment Semantics for Binary Code Search Binary code search plays a crucial role in applications like software reuse detection, and vulnerability identification. 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 relation 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 EESG nodes. 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.  ABOUT NDSS The Network and Distributed System Security Symposium (NDSS) fosters information exchange among researchers and practitioners of network and distributed system security. The target audience includes those interested in practical aspects of network and distributed system security, with a focus on actual system design and implementation. A major goal is to encourage and enable the Internet community to apply, deploy, and advance the state of available security technologies.  Our thanks to the Network and Distributed System Security (NDSS) Symposium for publishing their Creators, Authors and Presenter’s superb NDSS Symposium 2025 Conference content on the Organizations' YouTube Channel.  Permalink

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BinEnhance is a novel framework for binary code search that improves upon existing models by incorporating inter-function semantics through an External Environment Semantic Graph (EESG). Unlike traditional approaches that rely solely on internal code semantics or function call graphs, BinEnhance uses Relational Graph Convolutional Networks to learn external semantics and enhance code embeddings. When applied to existing models like HermesSim, Asm2vec, and Gemini, it improved Mean Average Precision from 53.6% to 69.7% while increasing efficiency fourfold, particularly excelling in function inlining scenarios.

NDSS 2025 – BinEnhance