PALMTREE: Learning an Assembly Language Model for Instruction Embedding

Deep learning has demonstrated its strengths in numerous binary analysis tasks, including function boundary detection, binary code search, function prototype inference, value set analysis, etc. When applying deep learning to binary analysis tasks, we need to decide what input should be fed into the neural network model. More specifically, we need to answer how to represent an instruction in a fixed-length vector. The idea of automatically learning instruction representations is intriguing, but the existing schemes fail to capture the unique characteristics of disassembly. These schemes ignore the complex intra-instruction structures and mainly rely on control flow in which the contextual information is noisy and can be influenced by compiler optimizations. In this paper, we propose to pre-train an assembly language model called PALMTREE for generating general-purpose instruction embeddings by conducting self-supervised training on large-scale unlabeled binary corpora. PALMTREE utilizes three pre-training tasks to capture various characteristics of assembly language. These training tasks overcome the problems in existing schemes, thus can help to generate high-quality representations. We conduct both intrinsic and extrinsic evaluations, and compare PALMTREE with other instruction embedding schemes. PALMTREE has the best performance for intrinsic metrics, and outperforms the other instruction embedding schemes for all downstream tasks.