Multi-Level Cross-Architecture Binary Code Similarity Metric

Abstract

Cross-architecture binary code similarity metric is a fundamental technique in many machine learning-based binary program analysis methods. Some researches recently utilize graph embedding methods to generate binary code embedding and regard Euclidean distance between two binary code as a similarity. However, these researches utilize manual features and do not make full use of binary code structure information, which causes the loss of binary code information. To solve above problems, we propose a multi-level neural network model to generate binary code embedding, which includes CFG(control flow graph) structure information and basic block information. We could measure the cross-architecture similarity through the Euclidean distance of binary code embedding. We conduct a series of experiments to compare the similarity of cross-architecture binary code, and the results demonstrate that our model can overcome the limitations described above and show superiority over the state-of-the-art methods.