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

在当今的数字时代，培养一支熟练的网络安全队伍对国家安全至关重要。传统的教育系统难以跟上新出现的威胁和多样化的学习要求。网络安全教育涉及复杂的工具和各种威胁场景，需要一种量身定制的渐进式学习方法，以有效地满足不同技能水平的需求。该项目开发用于网络安全教育的人工智能（AI）工具，使用安全知识图（AISecKG）增强的大型语言模型（LLM）来改善网络安全教育。该项目旨在（1）建立互动式教学方法，设计灵活和量身定制的学习策略，以适应本科生、研究生和专业学生的不同需求;和（2）通过提供自定进度的学习，个性化的支持和广泛的网络安全资源，在生成人工智能的帮助下，加强STEM的网络安全教育，使其更容易为广大受众所接受。该项目介绍了一种新颖的跨学科网络安全教育方法。首先，LLM和网络安全知识图将用于创建交互式工具。这些工具，如聊天机器人，旨在进行情境学习和模拟网络攻击。网络安全和人工智能专家将合作设计，验证和定制网络安全内容，以满足不同学习阶段的学生。 利用LLM和安全知识图表，内容将定期更新，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适应性学习体验，从而提高网络安全教育的可及性和有效性。人工智能和教育专家将合作并使用嵌入人工智能的度量系统来评估学生的认知参与度并衡量他们的学习成果。该项目的结构如下：（a）制定注重预期学习成果的问题导向学习课程;（B）在互动-建构-主动-被动网络安全问题导向学习教育学习框架内制定循证教学模块，以强调学生对学习任务的认知参与，提高学生解决不确定问题的自我效能，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，并在网络安全教育中负责任地使用人工智能技术来确保。该项目由安全和值得信赖的网络空间（SaTC）计划支持，该计划资助解决网络安全和隐私的建议，在这种情况下，特别是网络安全教育。SATC计划与联邦网络安全研究和发展战略计划和国家隐私研究战略保持一致，以保护和维护网络系统日益增长的社会和经济效益，同时确保安全和隐私。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。

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.