PalmTree: Learning an Assembly Language Model for Instruction Embedding Xuezixiang Li Yu Qu Heng Yin University of California Riverside University of California Riverside University of California Riverside Riverside, CA 92521, USA Riverside, CA 92521, USA Riverside, CA 92521, USA
[email protected] [email protected] [email protected] ABSTRACT into a neural network (e.g., the work by Shin et al. [37], UDiff [23], Deep learning has demonstrated its strengths in numerous binary DeepVSA [14], and MalConv [35]), or feed manually-designed fea- analysis tasks, including function boundary detection, binary code tures (e.g., Gemini [40] and Instruction2Vec [41]), or automatically search, function prototype inference, value set analysis, etc. When learn to generate a vector representation for each instruction using applying deep learning to binary analysis tasks, we need to decide some representation learning models such as word2vec (e.g., In- what input should be fed into the neural network model. More nerEye [43] and EKLAVYA [5]), and then feed the representations specifically, we need to answer how to represent an instruction ina (embeddings) into the downstream models. fixed-length vector. The idea of automatically learning instruction Compared to the first two choices, automatically learning representations is intriguing, but the existing schemes fail to capture instruction-level representation is more attractive for two reasons: the unique characteristics of disassembly. These schemes ignore the (1) it avoids manually designing efforts, which require expert knowl- complex intra-instruction structures and mainly rely on control flow edge and may be tedious and error-prone; and (2) it can learn higher- in which the contextual information is noisy and can be influenced level features rather than pure syntactic features and thus provide by compiler optimizations.