Software Vulnerability Analysis and Discovery Using Deep Learning Techniques: A Survey
Exploitable vulnerabilities in software have attracted tremendous attention in recent years because of their potentially high severity impact on computer security and information safety. Many vulnerability detection methods have been proposed to aid code inspection. Among these methods, there is a l...
Main Authors: | Peng Zeng, Guanjun Lin, Lei Pan, Yonghang Tai, Jun Zhang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2020-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9244140/ |
Similar Items
-
Survey on static software vulnerability detection for source code
by: LI Zhen, WANG Zeli, JIN Hai, et al.
Published: (2019-02-01) -
Investigating the impact of vulnerability datasets on deep learning-based vulnerability detectors
by: Lili Liu, et al.
Published: (2022-05-01) -
PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
by: Lu Wang, et al.
Published: (2020-11-01) -
Software Vulnerability Detection Using Informed Code Graph Pruning
by: Joseph Gear, et al.
Published: (2023-01-01) -
Smart Contract Vulnerability Detection Based on Deep Learning and Multimodal Decision Fusion
by: Weichu Deng, et al.
Published: (2023-08-01)