Collaborative Research: Modeling Student Activity and Learning on Cybersecurity Testbeds

协作研究：在网络安全测试平台上对学生活动和学习进行建模

• 批准号: 1723705
• 负责人: Richard Weiss
• 金额: $ 12.49万
• 依托单位: Evergreen State College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723705&HistoricalAwards=false
• 关键词: Collaborative; Research; Modeling; Student; Activity

This project led by the University of Southern California, in collaboration with Evergreen State College and Lewis and Clark College, aims to develop tools that automatically assess student learning in practical cybersecurity tasks, both for individual students and for the entire class. This is currently a challenge because such tasks are often open-ended and exploratory in nature. Because they can be completed in many ways, student learning in cybersecurity may not always be recognized by the instructor. This makes it hard to assess the sophistication of a student's path to success. Similarly, it may also be a challenge to identify the reasons why students experience failure, and to provide useful and timely feedback to students and instructors. The proposed project work will uncover reasons behind student underperformance in practical cybersecurity exercises, and will help identify effective interventions. The proposed research may also help network test beds retain and better serve their users. The impact of these activities will be three-fold. First, the developed tools will be integrated with two previously developed platforms for cybersecurity exercises: DeterLab and EDURange. This will directly impact approximately 2,000 students annually. Second, the tools will be highly portable to other platforms that use Linux in practical cybersecurity exercises, and can reach a wider audience. Third, the tools will help retain talent from disadvantaged and minority populations, as it will allow for earlier intervention and feedback to complete challenging, practical tasks. This project will develop ACSLE, a framework for automated assessment of student learning in practical cybersecurity exercises. ACSLE will engage in constant and extensive monitoring of student interaction with the computer, and will allow for the correlation with desired learning outcomes. ACSLE starts with the development of tools that monitor low-level student activities, such as commands typed, traffic generated and files and processes created. These low-level records are then synthesized into high-level indicators of student progress on a given cybersecurity task. The outcomes will allow for: (1) classification of students into several learning styles based on proficiency with a task and level of foundational skill; (2) clustering of solutions to specific learning challenges identified in student groups that have similar learning styles; (3) collection of successful learning paths developed over time and methods for identifying struggling students; (4) identification of causes of failure and delivery of appropriate learning interventions; and (5) aggregation of performance data for a class as well as identification of tasks that are difficult for many students. ACSLE will thus provide useful information for students and teachers, and improve overall learning in practical cybersecurity exercises.

该项目由南加州大学与常绿州立大学和刘易斯和克拉克学院合作，旨在开发工具，这些工具可以自动评估有关单个学生和整个班级的实用网络安全任务中学生学习的工具。目前，这是一个挑战，因为这些任务通常是开放的和探索性的。由于它们可以通过多种方式完成，因此教师可能并不总是认识到网络安全的学生学习。这使得很难评估学生成功之路的复杂性。同样，确定学生经历失败并为学生和教练提供有用和及时的反馈的原因也可能是一个挑战。拟议的项目工作将发现学生在实践网络安全练习中表现不佳的原因，并将有助于确定有效的干预措施。拟议的研究还可以帮助网络测试床保留并更好地为用户服务。这些活动的影响将为三倍。首先，开发的工具将与两个先前开发的用于网络安全练习的平台集成：Deterlab和Edurange。每年将直接影响约2,000名学生。其次，这些工具将非常适合其他在实践网络安全练习中使用Linux的平台，并且可以吸引更多的受众。第三，这些工具将有助于保留弱势群体和少数民族人口的人才，因为这将允许早期干预和反馈来完成具有挑战性的实际任务。该项目将开发Acsle，这是一个在实践网络安全练习中对学生学习自动评估的框架。 Acsle将不断地进行与计算机互动的持续和广泛的监控，并允许与所需的学习成果建立相关性。 acsle始于开发监视低级学生活动的工具，例如命令，流量生成的命令以及创建的文件和过程。然后将这些低级记录合成为在给定的网络安全任务上学生进步的高级指标。结果将允许：（1）根据熟练程度和一项任务和基础技能水平的熟练程度将学生分为几种学习风格； （2）在具有相似学习风格的学生群体中确定的特定学习挑战的解决方案； （3）随着时间的流逝而开发的成功学习道路的收集以及识别苦苦挣扎的学生的方法； （4）确定失败和交付适当学习干预措施的原因； （5）班级的绩效数据汇总以及对许多学生难以确定的任务。因此，Acsle将为学生和教师提供有用的信息，并改善实际网络安全练习中的整体学习。

项目成果

Using Terminal Histories to Monitor Student Progress on Hands-on Exercises

使用终端历史记录来监控学生的实践练习进度

• DOI: 10.1145/3328778.3366935
• 发表时间: 2020
• 期刊: Proceedings of ACM SIGCSE
• 作者: Mirkovic, Jelena;Aggarwal, Aashray;Weinman, David;Lepe, Paul;Mache, Jens;Weiss, Richard
• 通讯作者: Weiss, Richard

Measuring Student Learning On Network Testbeds

在网络测试平台上衡量学生的学习情况

• DOI: 10.1109/icnp.2019.8888101
• 发表时间: 2019
• 期刊: Proceedings of the 27th IEEE International Conference on Network Protocols
• 作者: Lepe, Paul;Aggarwal, Aashray;Mirkovic, Jelena;Mache, Jens;Weiss, Richard;Weinmann, David
• 通讯作者: Weinmann, David

Refactoring a Full Stack Web Application to Remove Barriers for Student Developers and to Add Customization for Instructors

重构全栈 Web 应用程序以消除学生开发人员的障碍并为教师添加自定义功能

• 发表时间: 2020
• 期刊: Journal of computing sciences in colleges
• 作者: Cook, Jack;Weiss, Richard;Mache, Jens
• 通讯作者: Mache, Jens

Predicting student success in cybersecurity exercises with a support vector classifier

• DOI: 10.5555/3447051.3447055
• 发表时间: 2020-01-01
• 期刊: Journal of Computing Sciences in Colleges
• 作者: Vinlove, Q.;Mache, J.;Weiss, R.
• 通讯作者: Weiss, R.

HANDS-ON CYBERSECURITY EXERCISES

网络安全实践练习

• 发表时间: 2018
• 期刊: Journal of computing sciences in colleges
• 作者: Mache, Jens;Weiss, Richard
• 通讯作者: Weiss, Richard