A Type Information Reconstruction Scheme Based on Long Short-Term Memory for Weakness Analysis in Binary File

摘要

Due to increasing use of third-party libraries because of the increasing complexity of softwaxe development, the lack of management of legacy code and the nature of embedded software, the use of third-party libraries which have no source code is increasing. Without the source code, it is difficult to analyze these libraries for vulnerabilities. Therefore, to analyze weaknesses inherent in binary code, various studies have been conducted to perform static analysis using intermediate code. The conversion from binary code to intermediate language differs depending on the execution environment. In this paper, we propose a deep learning-based analysis method to reconstruct missing data types during the compilation process from binary code to intermediate language, and propose a method to generate supervised learning data for deep learning.