Using Machine Learning to Provide Students with Rapid Feedback during Hands-on Cybersecurity Exercises

使用机器学习在网络安全实践练习中为学生提供快速反馈

• 批准号: 2216485
• 负责人: Jens Mache
• 金额: $ 13.82万
• 依托单位: Lewis and Clark College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216485&HistoricalAwards=false
• 关键词: Using; Machine; Learning; Provide; Students

This project aims to serve the national interest by using machine learning to provide students with timely feedback while completing hands-on cybersecurity exercises. A cyber informed citizenry is a vital part of our national defense strategy. Cybercrime is becoming increasingly sophisticated, and the systems and devices that need security are becoming ever more complex and interconnected. These issues highlight the need to develop programs that enable students to quickly obtain the fundamental skills and knowledge considered essential by cybersecurity experts. Hands-on cybersecurity exercises are known to provide students with basic cybersecurity knowledge, skills, and abilities. To be effective these exercises need to provide students with rapid feedback to prevent them from getting stuck and frustrated. The goal of this project is to use machine learning to monitor students as they work through hands-on cybersecurity exercises and automatically identify when they are getting stuck and frustrated. The students will then be given suggestions to help them to successfully complete the exercise. This project plans to use reinforcement learning to create, test, and deploy a semi-automated rapid hint system. The project intends to develop tools to collect hints directly from student-teacher interactions, which will then be used to teach the system which hints to apply and when. The system will interact with both the teacher and the student by suggesting hints as the system becomes more proficient. The hint system will be integrated into the EDURange platform but will be compatible with other cyberrange platforms such as DeterLab and KYPO. The PI team intends to offer workshops for faculty on how to use EDURange and the hint system. The hint system will collect data that will be analyzed to determine the efficacy of the tool, and to develop new hints and strategies for helping students. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过使用机器学习为学生提供及时的反馈，同时完成动手的网络安全练习，为国家利益服务。网络信息公民是我们国防战略的重要组成部分。网络犯罪变得越来越复杂，需要安全的系统和设备变得越来越复杂和相互关联。这些问题突出表明，有必要制定计划，使学生能够快速获得网络安全专家认为必不可少的基本技能和知识。众所周知，动手网络安全练习可以为学生提供基本的网络安全知识，技能和能力。为了有效，这些练习需要为学生提供快速反馈，以防止他们陷入困境和沮丧。该项目的目标是使用机器学习来监控学生，因为他们通过动手网络安全练习工作，并自动识别他们何时陷入困境和沮丧。然后，学生将得到建议，以帮助他们成功地完成练习。该项目计划使用强化学习来创建，测试和部署半自动化快速提示系统。该项目旨在开发工具，直接从学生和教师的互动中收集提示，然后将用于教授系统何时应用提示。随着系统变得更加熟练，系统将通过建议提示与教师和学生进行交互。提示系统将被集成到EDURange平台中，但将与其他cyberrange平台兼容，如DeterLab和KYPO。 PI团队打算为教师提供如何使用EDURange和提示系统的研讨会。提示系统将收集数据，并对这些数据进行分析，以确定该工具的有效性，并开发新的提示和策略来帮助学生。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Hands-On SQL Injection in the Classroom: Lessons Learned

课堂上的 SQL 注入实践：经验教训

• 发表时间: 2022
• 期刊: Journal of computing sciences in colleges
• 作者: Mache, J.;Richardson, N.;Greenlaw Rollins, W.;García Morán, C.;Weiss, R.
• 通讯作者: Weiss, R.