Hierarchical graph neural network for cross-architectural software reverse engineering

Abstract

Recovering symbols from a stripped binary includes representing the stripped binary as a plurality of graph of graphs (GoG) representations, converting the plurality of GoGs into a plurality of expressive representations of each function in the stripped binary, training a machine learning (ML) model using the expressive representations, and determining a missing symbol of at least one of the functions based on an output of the ML model. Information relating to functions is and interactions between functions are used to train an ML to determine similarity of two functions. Based on similarities, missing symbols may be inferred and used to enable updates to the stripped binary file where source code is not available.

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