Open Access

ARTICLE

A Survey on Binary Code Vulnerability Mining Technology

by Pengzhi Xu, Zetian Mai, Yuhao Lin, Zhen Guo, Victor S. Sheng

1 School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, China
2 Key Laboratory of Internet Information Retrieval of Hainan Province, Haikou, China
3 Department of Computer Science Texas Tech University, Texas, USA

* Corresponding Author: Zhen Guo. Email:

Journal of Information Hiding and Privacy Protection 2021, 3(4), 165-179. https://doi.org/10.32604/jihpp.2021.027280

Received 13 January 2022; Accepted 02 March 2022; Issue published 22 March 2022

Abstract

With the increase of software complexity, the security threats faced by the software are also increasing day by day. So people pay more and more attention to the mining of software vulnerabilities. Although source code has rich semantics and strong comprehensibility, source code vulnerability mining has been widely used and has achieved significant development. However, due to the protection of commercial interests and intellectual property rights, it is difficult to obtain source code. Therefore, the research on the vulnerability mining technology of binary code has strong practical value. Based on the investigation of related technologies, this article firstly introduces the current typical binary vulnerability analysis framework, and then briefly introduces the research background and significance of the intermediate language; with the rise of artificial intelligence, a large number of machine learning methods have been tried to solve the problem of binary vulnerability mining. This article divides the current related binary vulnerabilities mining technology into traditional mining technology and machine learning mining technology, respectively introduces its basic principles, research status and existing problems, and briefly summarizes them. Finally, based on the existing research work, this article puts forward the prospect of the future research on the technology of binary program vulnerability mining.

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Keywords

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