SaTC: EDU: AI for Cybersecurity Education via an LLM-enabled Security Knowledge Graph

SaTC：EDU：通过支持 LLM 的安全知识图进行网络安全教育的人工智能

• 批准号: 2335666
• 负责人: Huan Liu
• 金额: $ 50万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2335666&HistoricalAwards=false
• 关键词: SaTC; EDU; AI; Cybersecurity; Education

Developing a skilled cybersecurity workforce is critical for national security in today’s digital age. Traditional education systems struggle to keep pace with emerging threats and diverse learning requirements. Cybersecurity education, involving complex tools and varied threat scenarios, requires a tailored, progressive learning approach to effectively cater to different skill levels. This project develops Artificial Intelligence (AI) tools for cybersecurity education using large language models (LLMs) augmented with a Security Knowledge Graph (AISecKG) to improve cybersecurity education. The project aims to (1) establish interactive teaching methods and design flexible and tailored learning strategies to suit the diverse needs of undergraduate, graduate, and professional students; and (2) enhance cybersecurity education in STEM by offering self-paced learning, personalized support, and extensive cybersecurity resources, with the assistance of generative AI, making it more accessible to a broad audience.This project introduces a novel, interdisciplinary approach to cybersecurity education. First, LLMs and cybersecurity knowledge graphs will be utilized to create interactive tools. These tools, such as chatbots, are designed for contextual learning and simulating cyber-attacks. Cybersecurity and AI experts will collaborate to design, validate, and tailor the cybersecurity content to cater to students at various learning stages. Leveraging LLMs and security knowledge graphs, the content will be regularly updated to reflect the latest cybersecurity trends and advancements. The interactive educational tools will engage the students with adaptive learning experiences, thereby improving accessibility and effectiveness of cybersecurity education. The AI and education experts will collaborate and use an AI-embedded metric system to assess students' cognitive engagement and measure the outcomes of their learning. This project will be structured as follows: (a) Develop a problem-based learning (PBL) curriculum focused on desired learning outcomes; (b) Develop evidence-based teaching modules within the Interactive-Constructive-Active-Passive (ICAP) learning framework for PBL cybersecurity education to emphasize student cognitive engagement in learning tasks, enhance student self-efficacy for navigating uncertain problems, and promote student learning outcomes; (c) Integrate learning and assessment modules with predictive analytics to identify the students at risk and provide appropriate and timely support for early intervention. Students' data security, privacy, and transparency will be ensured by designing ethical and explainable frameworks and responsible use of AI technologies in cybersecurity education.This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity education. The SaTC program aligns with the Federal Cybersecurity Research and Development Strategic Plan and the National Privacy Research Strategy to protect and preserve the growing social and economic benefits of cyber systems while ensuring security and privacy.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.

在当今的数字时代，发展熟练的网络安全劳动力对国家安全至关重要。传统的教育系统努力与新兴威胁和多样化的学习要求保持同步。网络安全教育涉及复杂的工具和各种威胁情景，需要一种量身定制的渐进学习方法，以有效地满足不同的技能水平。该项目开发了使用安全知识图（Aiseckg）增强的大型语言模型（LLM）来开发网络安全教育的工具，以改善网络安全教育。该项目旨在（1）建立互动教学方法，并设计灵活，量身定制的学习策略，以适应本科，研究生和专业学生的潜水员需求； （2）通过提供自定义的学习，个性化的支持和广泛的网络安全资源来增强STEM的网络安全教育，并在通用AI的帮助下，使广泛受众更容易获得。该项目引入了一种新颖的跨学科方法来实现网络安全教育。首先，将利用LLMS和网络安全知识图来创建交互式工具。这些工具（例如聊天机器人）专为上下文学习和模拟网络攻击而设计。网络安全和AI专家将合作设计，验证和量身定制网络安全内容，以适应各个学习阶段的学生。利用LLM和安全知识图，将定期更新内容，以反映最新的网络安全趋势和进步。互动教育工具将使学生获得自适应学习经验，从而提高网络安全教育的可及性和有效性。 AI和教育专家将合作并使用AI填充的度量系统来评估学生的认知参与并衡量学习的结果。该项目的结构如下：（a）开发基于问题的学习（PBL）课程，该课程侧重于所需的学习成果； （b）为PBL网络安全教育的交互式构建激活（ICAP）学习框架开发基于循证的教学模块，以强调学生在学习任务中的认知参与，增强学生自我效能，以导致不确定的问题导航，并促进学生学习成果； （c）将学习和评估模块与预测分析相结合，以确定有风险的学生，并为早期干预提供适当和及时的支持。学生的数据安全，隐私和透明度将通过设计道德和可解释的框架以及负责在网络安全教育中负责使用AI技术。该项目得到了安全且可信赖的网络空间（SATC）计划的支持，该计划资助了这些建议，这些建议资助了这些建议，这些建议是针对这种案例的网络安全性和隐私权，以及在这种情况下，以及Cyber​​security教育。 SATC计划与联邦网络安全研究与发展战略计划以及国家隐私研究战略保持一致，以保护和维护网络系统的不断增长的社会和经济利益，同时确保安全和隐私。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的知识分子和更广泛影响的评估来审查Criteria来通过评估来通过评估来支持的。