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.

培养熟练的网络安全劳动力对于当今数字时代的国家安全至关重要。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，需要量身定制的渐进式学习方法，以有效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，使用大型语言模型 (LLM) 和安全知识图 (AISecKG) 进行增强，以改善网络安全教育。该项目旨在（1）建立互动式教学方法，设计灵活、量身定制的学习策略，以满足本科生、研究生和专业学生的多样化需求； (2) 在生成式人工智能的帮助下，通过提供自定进度的学习、个性化支持和广泛的网络安全资源，加强 STEM 的网络安全教育，使其更容易为广大受众所接受。该项目引入了一种新颖的跨学科网络安全教育方法。首先，法学硕士和网络安全知识图将用于创建交互式工具。这些工具（例如聊天机器人）专为情境学习和模拟网络攻击而设计。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的需求。 利用法学硕士和安全知识图，内容将定期更新，以反映最新的网络安全趋势和进展。交互式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人工智能嵌入式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) 开发基于问题的学习（PBL）课程，重点关注期望的学习成果； (b) 在交互式-建设性-主动-被动（ICAP）学习框架内开发基于证据的教学模块，用于 PBL 网络安全教育，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，并促进学生的学习成果； (c) 将学习和评估模块与预测分析相结合，以识别面临风险的学生，并为早期干预提供适当和及时的支持。通过设计道德和可解释的框架以及在网络安全教育中负责任地使用人工智能技术，将确保学生的数据安全、隐私和透明度。该项目得到安全可信网络空间（SaTC）计划的支持，该计划资助解决网络安全和隐私问题的提案，在本例中特别是网络安全教育。 SaTC 计划与联邦网络安全研究与发展战略计划和国家隐私研究战略相一致，旨在保护和维护网络系统不断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智力价值和更广泛的影响审查标准进行评估，被认为值得支持。