Abstract: Rich readable source information is important for reverse work,especially high-quality function names are important for program understanding.However,software publishers often release executable stripped of source-level debugging information,either to prevent reversals or to streamline the size of the software,which makes reverse analysis more difficult due to the lack of readable information.To this end,a multi-architecture function name prediction(MFNP) method is proposed to resolve the differences between architectures using LLVM RetDec to decompile X86,ARM,and MIPS architecture binaries into intermediate language(IR) files.Morphological and semantic similarity comparison of function names in readable intermediate language.ll files,and similarity fusion of function names to reduce function name data sparsity.The basic blocks carrying the semantic information of sequential instructions and the control flow graph of function bodies with basic blocks as the basic units are used as semantic features of function bodies,combined with neural networks to achieve function name prediction of stripped binaries in three architectures,X86,MIPS and ARM.Compared to DEBIN,it additionally supports stripped binary function name prediction work under MIPS architecture DEBIN.The improvement in Precision and F1 is 13.86% and 11.93% respectively compared with NERO.The effectiveness of MFNP in selecting sequential instruction sequences and control flow graphs extracted with basic blocks as the basic unit as semantic features is verified.

Key words: Static binary analysis, Intermediate representations, Function name prediction, Natural language processing, Neural networks