MSSA: multi-stage semantic-aware neural network for binary code similarity detection

Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. Current state-of-the-art approaches are based on Transformer, which require substantial computation resources. Learning-based approaches remains room for optimization in learning the deeper semantics of binary code. In this paper, we propose MSSA, a multi-stage semantic-aware neural network for BCSD at the function level. It effectively integrates the semantic and structural information of assembly instructions within and between basic blocks, and across the entire function through four semantic-aware neural networks, achieving deep understanding of binary code semantics. MSSA is a lightweight model with only 0.38M parameters in its backbone network, suitable for deployment in CPU environments. Experimental results show that MSSA outperforms Gemini, Asm2Vec, SAFE, and jTrans in classification performance and ranks second only to the Transformer-based jTrans in retrieval performance.