.png){kind=icon}
返回
Context-Aware Software Vulnerability Classification Using Machine Learning
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2021.3075385.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Managing the vulnerabilities reported by a number of security scanning software is a tedious and time-consuming task, especially in large-scale, modern communication networks. Particular software vulnerabilities can have a range of impacts on an IT system depending on the context in which they were detected. Moreover, scanning software can report thousands of issues, which makes performing operations, such as analysis and prioritization, very costly from an organizational point of view. In this paper, we propose a context-aware software vulnerability classification system, Mixeway, that relies on machine learning to automatize the whole process. By training a model using known and analyzed vulnerabilities together with Natural Language Processing techniques to properly manage the information that the vulnerability description contains, we show that it is possible to predict the class that defines how severe the detected vulnerability is. The experimental results obtained on a real-life dataset collected by Mixeway for about 12 months from the infrastructure of one of the major mobile network operators in Poland prove that the proposed solution is useful and effective.
Keyword:
Software
Security
Machine learning algorithms
Machine learning
Support vector machines
Software algorithms
Prediction algorithms
IT security
devsecops
machine learning
classification
vulnerability classification
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
引用论文
.png){kind=icon}
分享收藏Software Vulnerability Analysis and Discovery Using Machine-Learning and Data-Mining Techniques: A Survey使用机器学习和数据挖掘技术进行软件漏洞分析和发现: 调查
PREAI
分享收藏
查看更多内容