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

EDU：网络安全学习和实验自动化平台 (AutoCUE)

• 批准号: 1623253
• 负责人: Vassil Roussev
• 金额: $ 30万
• 依托单位: University of New Orleans
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1623253&HistoricalAwards=false
• 关键词: EDU; Automated; Platform; Cyber; Security

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.

在网络安全课程中提供广泛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，这一障碍有效地限制了纳入网络安全教育的实践工作量。该项目旨在消除这些障碍，并通过自动化最耗时的任务来大大提高教育过程的效率。该项目对网络安全教育做出了两个主要贡献：开发一个规范驱动的动态环境，用于实施现实的网络防御和取证分析练习;以及对班级管理和自动化评估的高级支持。AutoCUE平台提供了一种高级规范语言和一个执行运行时，使教师能够轻松有效地运行逼真的场景，从而产生定制的环境;基于相同的方法，该系统还可以用于自动创建逼真的实验数据集。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（用于评价目的）;以及c）自动评分模块，其结合来自夺旗比赛和环境传感器的想法来跟踪学生的进步并使评分过程自动化。该项目还为两个课程提供了现成的种子内容：数字取证和网络渗透测试。