.png){kind=icon}
返回
Vulnerability Prediction From Source Code Using Machine Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.3016774.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
As the role of information and communication technologies gradually increases in our lives, software security becomes a major issue to provide protection against malicious attempts and to avoid ending up with noncompensable damages to the system. With the advent of data-driven techniques, there is now a growing interest in how to leverage machine learning (ML) as a software assurance method to build trustworthy software systems. In this study, we examine how to predict software vulnerabilities from source code by employing ML prior to their release. To this end, we develop a source code representation method that enables us to perform intelligent analysis on the Abstract Syntax Tree (AST) form of source code and then investigate whether ML can distinguish vulnerable and nonvulnerable code fragments. To make a comprehensive performance evaluation, we use a public dataset that contains a large amount of function-level real source code parts mined from open-source projects and carefully labeled according to the type of vulnerability if they have any.We show the effectiveness of our proposed method for vulnerability prediction from source code by carrying out exhaustive and realistic experiments under different regimes in comparison with state-of-art methods.
Keyword:
Software
Security
Natural language processing
Syntactics
Predictive models
Software metrics
Machine learning
AST
machine learning
source code
vulnerability prediction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
Stress Corrosion Cracking in Al-Zn-Mg-Cu Aluminum Alloys in Saline EnvironmentsAl-zn-mg-cu铝合金在盐碱环境中的应力腐蚀开裂
PREAI
分享收藏
查看更多内容