A Vulnerability Detection System Based on Fusion of Assembly Code and Source Code. (30th July 2021)

• Record Type: Journal Article
• Authors: Li, Xingzheng; Feng, Bingwen; Li, Guofeng; Li, Tong; He, Mingjin
• Other Names: Zhang Liguo Academic Editor.
• Is Part Of: Security and communication networks. Volume 2021(2021)
• Publication Date: 2021-07-30
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2021/9997641
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Software vulnerabilities are one of the important reasons for network intrusion. It is vital to detect and fix vulnerabilities in a timely manner. Existing vulnerability detection methods usually rely on single code models, which may miss some vulnerabilities. This paper implements a vulnerability detection system by combining source code and assembly code models. First, code slices are extracted from the source code and assembly code. Second, these slices are aligned by the proposed code alignment algorithm. Third, aligned code slices are converted into vector and input into a hyper fusion-based deep learning model. Experiments are carried out to verify the system. The results show that the system presents a stable and convergent detection performance.