PalmTree：学习用于指令嵌入的汇编语言模型
PalmTree: Learning an Assembly Language Model for Instruction Embedding
摘要：
深度学习已经在众多的二进制分析任务中展示了其优势，包括函数边界检测，二进制代码搜索，函数原型推理，值集分析等。当将深度学习应用于二进制分析任务时，我们需要决定应该将什么输入输入到神经网络模型中。更具体地说，我们需要回答如何在固定长度向量中表示指令。自动学习指令表示的想法是有趣的，然而现有的计划未能捕捉拆卸的独特特性。这些方案忽略了复杂的指令内结构，主要依赖于控制流，其中上下文信息是嘈杂的，可以通过编译器优化的影响。在本文中，我们提出了预训练的汇编语言模型称为棕榈树生成通用指令嵌入进行自我监督训练的大规模未标记的二进制语料库。PalmTree利用三个预训练任务来捕获汇编语言的各种特征。这些训练任务克服了现有方案中存在的问题，从而有助于生成高质量的表示。我们进行内在和外在的评价，并比较棕榈树与其他指令嵌入方案。PalmTree具有最好的内在指标的性能，并优于其他指令嵌入方案的所有下游任务。
英文摘要：
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, however 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
