论文标题
使用NLP自动识别安全相关配置设置
Automated Identification of Security-Relevant Configuration Settings Using NLP
论文作者
论文摘要
为了保护计算机基础架构，我们需要配置所有与安全相关的设置。我们需要安全专家来确定与安全相关的设置，但是此过程耗时且昂贵。我们提出的解决方案使用最先进的自然语言处理来根据其描述将设置分类为与安全性相关的。我们的评估表明，我们训练有素的分类器的表现不足以取代人类安全专家，但可以帮助他们对设置进行分类。通过发布我们的标签数据集和训练有素的模型的代码，我们希望帮助安全专家分析配置设置并在该领域进行进一步的研究。
To secure computer infrastructure, we need to configure all security-relevant settings. We need security experts to identify security-relevant settings, but this process is time-consuming and expensive. Our proposed solution uses state-of-the-art natural language processing to classify settings as security-relevant based on their description. Our evaluation shows that our trained classifiers do not perform well enough to replace the human security experts but can help them classify the settings. By publishing our labeled data sets and the code of our trained model, we want to help security experts analyze configuration settings and enable further research in this area.
