A Review of Deep Learning-Based Binary Code Similarity Analysis

Abstract

Against the backdrop of highly developed software engineering, code reuse has been widely recognized as an effective strategy to significantly alleviate the burden of development and enhance productivity. However, improper code citation could lead to security risks and license issues. With the source codes of many pieces of software being difficult to obtain, binary code similarity analysis (BCSA) has been extensively implemented in fields such as bug search, code clone detection, and patch analysis. This research selects 39 papers on BCSA from top-tier and emerging conferences within artificial intelligence, network security, and software engineering from 2016 to 2022 for deep analysis. The central focus lies on methods utilizing deep learning technologies, detailing a thorough summary and the arrangement of the application and implementation specifics of various deep learning technologies. Furthermore, this study summarizes the research patterns and development trends in this field, thereby proposing potential directions for future research.