Education DCL: EAGER: Re-imagining the Role of Humans in Security Education

教育 DCL：EAGER：重新想象人类在安全教育中的角色

• 批准号: 2335633
• 负责人: Wu-chang Feng
• 金额: $ 26.29万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335633&HistoricalAwards=false
• 关键词: Education; DCL; EAGER; Re; imagining

Generative large language models (LLMs) are transforming the field of security with their ability to provide fast and comprehensive insights and information. LLMs are now being applied to several important security tasks, including platform-specific incident response, secure programming, binary analysis, and penetration testing. With automation changing the practice of security as we know it, this project seeks to refresh security education in order to better align what we teach students to how security is now practiced. The project's novelties include creating a new curriculum that embraces the use of LLMs throughout a student's cybersecurity education in a way that prepares them to meet the demands of future security workforce. In doing so, the project's broader significance and importance will be to enable a broader pipeline of students who can leverage the use of modern tools such as LLMs to solve cybersecurity problems, including those with less technical backgrounds and those from underrepresented groups.The project's approach for redesigning security curricula centers around the planned restructuring of security curricula around LLMs. The new curriculum addresses different aspects of applying LLMs to solving security problems: what to ask for, which model to ask, how to ask for it, whether to task for it, and whether the result is trustworthy. By weaving this into traditional security courses in computer, network, web, and cloud security, students will gain important insight and practical skills that they can then use to contribute to the modern security workforce. To ensure the results of this project are broadly accessible, all curricula and lab exercises will be made available via public repositories.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.

生成式大型语言模型（LLM）正在通过提供快速、全面的见解和信息来改变安全领域。LLM现在被应用于几个重要的安全任务，包括特定于平台的事件响应，安全编程，二进制分析和渗透测试。随着自动化改变我们所知道的安全实践，这个项目旨在更新安全教育，以便更好地将我们教学生的内容与现在的安全实践相结合。 该项目的新颖之处包括创建一个新的课程，该课程包括在学生的网络安全教育中使用LLM，使他们能够满足未来安全劳动力的需求。 在此过程中，该项目的更广泛意义和重要性将是为能够利用LLM等现代工具解决网络安全问题的学生提供更广泛的渠道，包括那些技术背景较低和代表性不足的群体。该项目重新设计安全课程的方法围绕着计划中的LLM安全课程重组。 新课程解决了应用LLM解决安全问题的不同方面：要求什么，要求哪个模型，如何要求它，是否任务，以及结果是否值得信赖。 通过将其融入计算机，网络，Web和云安全的传统安全课程，学生将获得重要的洞察力和实用技能，然后可以用于为现代安全劳动力做出贡献。 为了确保该项目的成果能够被广泛获取，所有课程和实验练习都将通过公共资源库提供。该奖项反映了NSF的法定使命，并被认为值得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来支持。