命令行界面风险建模

摘要：

保护敏感数据是云计算安全性的重要组成部分。但是，只有特定的特权人员才能访问这些数据或与之交互;因此，依赖这些人员来维护软件是不可扩展的。对此的解决方案是允许非特权个人访问以维护这些系统，但屏蔽敏感信息以免流出。为此，我们创建了一个机器学习模型来预测和编辑包含敏感数据的字段。本文主要介绍Azure PowerShell，展示它如何应用于其他命令行界面和API。使用F5分数作为加权度量，我们展示了不同的转换技术，将这个问题从一个未知的领域映射到自然语言处理的研究领域。

英文摘要：

Protecting sensitive data is an essential part of security in cloud computing. However, only specific privileged individuals have access to view or interact with this data; therefore, it is unscalable to depend on these individuals also to maintain the software. A solution to this is to allow non-privileged individuals access to maintain these systems but mask sensitive information from egressing. To this end, we have created a machine-learning model to predict and redact fields with sensitive data. This work concentrates on Azure PowerShell, showing how it applies to other command-line interfaces and APIs. Using the F5-score as a weighted metric, we demonstrate different transformation techniques to map this problem from an unknown field to the well-researched area of natural language processing.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。