Reviewed Version (pdf): https://openreview.net/references/pdf?id=yyKS6n7L-K
Keywords: Programming Language, Reverse engineering, neural machine translation, machine learning for system
Abstract: Binary decompilation is a powerful technique for analyzing and understanding software, when source code is unavailable. It is a critical problem in the computer security domain. With the success of neural machine translation (NMT), recent efforts on neural-based decompiler show promising results compared to traditional approaches. However, several key challenges remain: (i) Prior neural-based decompilers focus on simplified programs without considering sophisticated yet widely-used data types such as pointers; furthermore, many high-level expressions map to the same low-level code (expression collision), which incurs critical decompiling performance degradation; (ii) State-of-the-art NMT models(e.g., transformer and its variants) mainly deal with sequential data; this is inefficient for decompilation, where the input and output data are highly structured. In this paper, we propose N-Bref, a new framework for neural decompilers that addresses the two aforementioned challenges with two key design principles: (i)N-Bref designs a structural transformer with three key design components for better comprehension of structural data – an assembly encoder, an abstract syntax tree encoder, and a tree decoder, extending transformer models in the context of decompilation. (ii) N-Bref introduces a program generation tool that can control the complexity of code generation and removes expression collisions. Extensive experiments demonstrate that N-Bref outperforms previous neural-based decompilers by a margin of 6.1%/8.8% accuracy in datatype recovery and source code generation. In particular, N-Bref decompiled human-written Leetcode programs with complex library calls and data types in high accuracy.
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