BETAC: Bidirectional Encoder Transformer for Assembly Code Function Name Recovery

Recovering function names from stripped binaries is a crucial and time-consuming task for software reverse engineering' particularly in enhancing network reliability, resilience, and security. This paper tackles the challenge of recovering function names in stripped binaries, a fundamental step in reverse engineering. The absence of syntactic information and the possibility of different code producing identical behavior complicate this task. To overcome these challenges, we introduce a novel model, the Bidirectional Encoder Transformer for Assembly Code (BETAC), leveraging a transformer-based architecture known for effectively processing sequential data. BETAC utilizes self-Attention mechanisms and feed-forward networks to discern complex relationships within assembly code for precise function name prediction. We evaluated BETAC against various existing encoder and decoder models in diverse binary datasets, including benign and malicious codes in multiple formats. Our model demonstrated superior performance over previous techniques in certain metrics and showed resilience against code obfuscation.