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 featu
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