EDU: Automated Platform for Cyber Security Learning and Experimentation (AutoCUE)

在网络安全课程中提供广泛的实践经验的主要障碍之一是创建和评分练习所涉及的大量手工工作。再加上经常需要更新练习，这一障碍实际上限制了纳入网络安全教育的动手工作量。该项目旨在消除这些障碍，并通过自动化最耗时的任务来极大地提高教育过程的效率。该项目对网络安全教育有两个主要贡献：为实施现实的网络防御和取证分析练习开发一个规范驱动的动态环境；以及为班级管理和自动评估提供高级支持。AutoCue平台提供了高级规范语言和执行运行时，使教师能够轻松高效地运行导致定制环境的现实场景；基于相同的方法，该系统还可用于自动创建真实的实验数据集。该基础设施提供了一个自动化的班级管理组件，它包括：a)部署自动化模块，它保证了一致的学生实验室环境，并由教师进行集中控制；b)场景个性化模块，它可以为每个学生生成定制的练习(用于评估目的)；c)自动评分模块，它结合了夺旗比赛和环境传感器的想法，以跟踪学生的进度并自动评分过程。该项目还为两个类别提供了随时可用的种子内容：数字取证和网络渗透测试。

One of the main obstacles in providing extensive hands-on experience in cybersecurity classes is the substantial amount of manual work involved in creating and grading the exercise. Combined with the frequent need to update the exercises, this obstacle effectively limits that amount of hands-on work that gets incorporated into cybersecurity education. This project seeks to eliminate such barriers, and to greatly improve the efficiency of the educational process by automating the most time-consuming tasks. This project makes two main contributions to cybersecurity education: the development of a specification-driven, dynamic environment for implementing realistic cyber defense and forensic analysis exercises; and the advanced support for class management and automated evaluation. The platform, AutoCUE, provides a high-level specification language, and an execution runtime that enable instructors to easily and efficiently run realistic scenarios that result in customized environments; based on the same methods, the system also be used to automatically create of realistic experimental data sets. The infrastructure provides an automated class management component, which consists of: a) deployment automation module, which guarantees consistent student lab environment, and central control by the instructor; b) scenario personalization module, which can generate customized exercises for each student (for evaluation purposes); and c) automated grading module, which combines ideas from capture-the-flag competitions and environment sensors to track student progress and automate the grading process. The project also provides ready-to-use seed content for two classes: digital forensics and network penetration testing.