EAGER: SaTC-EDU: Artificial Intelligence for Cybersecurity Education via a Machine Learning-Enabled Security Knowledge Graph

EAGER：SaTC-EDU：通过机器学习支持的安全知识图进行网络安全教育的人工智能

• 批准号: 2114789
• 负责人: Huan Liu
• 金额: $ 30万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114789&HistoricalAwards=false
• 关键词: EAGER; SaTC; EDU; Artificial; Intelligence

Cybersecurity education is exceptionally challenging because its learning outcomes often comprise fragmented information that fails to provide learners with adaptive guidance on how to connect and build on the concepts they have learned. This project will develop an artificial intelligence (AI)-enabled cybersecurity tool referred to as a knowledge graph (AISecKG) to address this cybersecurity education challenge. Knowledge graphs, widely used by search engines and social networks, integrate data and can store linked descriptions of items such as objects, concepts, and events. This project applies a novel learning approach for cybersecurity education by providing university students a flexible learning plan that enhances their critical thinking and problem-solving skills. This approach aims to help students understand the complex nature of cyber-attacks and defense mechanisms, provide them with a holistic view and better prepare them to address the complexities of real-world scenarios. The development and deployment of AISecKG are interdisciplinary. First, the project employs machine learning (ML) and AI approaches to build a new cybersecurity knowledge graph by measuring and setting up similarities and dependencies among cybersecurity learning targets for both study planning and learning-outcome assessment. Second, it incorporates a multi-level assessment approach to design cybersecurity curricula, scaffold student cognitive engagement, and improve student learning outcomes. AISecKG has two primary design goals. First, it will guide instructors to develop a problem-based learning curriculum based on their learning objectives. Second, it will allow students to apply an adaptive learning strategy, incorporating hands-on labs to assess their learning outcomes. To assess students’ learning performance quantitatively, AISecKG will (a) deploy an evidence-based model and learning materials for problem-based cybersecurity education focusing on developing teacher capacity and practice while using targeted materials and approaches; (b) produce a productive teaching model for deep learning that promotes a culture of scientific inquiry and design as well as a set of strategies to develop student competency; and (c) provide evidence of student learning outcomes as a pedagogical resource to support student cognitive engagement in learning tasks interactively. This project is supported by a special initiative of the Secure and Trustworthy Cyberspace (SaTC) program to foster new, previously unexplored, collaborations between the fields of cybersecurity, artificial intelligence, and 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.

网络安全教育极具挑战性，因为其学习成果通常包含碎片化信息，无法为学习者提供如何连接和构建他们所学概念的自适应指导。该项目将开发一种支持人工智能 (AI) 的网络安全工具。作为解决网络安全教育挑战的知识图谱（AISecKG），知识图谱被搜索引擎和社交网络广泛使用，可以集成数据并可以存储对象、概念和事件等项目的链接描述。该项目应用了一种新颖的学习方式。通过提供大学网络安全教育的方法为学生提供灵活的学习计划，增强他们的批判性思维和解决问题的能力。这种方法旨在帮助学生了解网络攻击和防御机制的复杂性，为他们提供整体观点，并更好地为他们解决现实的复杂性做好准备。 AISecKG 的开发和部署是跨学科的，该项目采用机器学习 (ML) 和人工智能方法，通过测量和建立网络安全学习目标之间的相似性和依赖性来构建新的网络安全知识图。其次，学习成果评估。 AISecKG 采用多层次评估方法来设计网络安全课程，提高学生的认知参与度并提高学生的学习成果，其主要设计目标有两个：第一，它将指导教师根据其学习目标开发基于问题的学习课程。其次，它将允许学生应用适应性学习策略，结合动手实验室来评估他们的学习成果。为了定量评估学生的学习表现，AISecKG 将 (a) 部署基于证据的模型和基于问题的学习材料。网络安全教育注重发展教师的能力和实践，同时使用有针对性的材料和方法；(b) 制定有效的深度学习教学模式，促进科学探究和设计文化以及一套培养学生能力的策略；(c) 提供证据学生的学习成果作为一种教学资源，支持学生以交互方式参与学习任务。该项目得到了安全可信网络空间（SaTC）计划的一项特别倡议的支持，以促进网络安全、人工智能等领域之间新的、以前未探索过的合作。智力，以及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，并被视为值得通过使用基金会的智力优点和更广泛的影响审查标准进行评估来支持。

项目成果

Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey

知识图谱可以减少法学硕士的幻觉吗？

• DOI: 10.48550/arxiv.2311.07914
• 发表时间: 2023-11-14
• 期刊: ArXiv
• 作者: Garima Agrawal;Tharindu Kumarage;Zeyad Alghami;Huanmin Liu
• 通讯作者: Huanmin Liu

Development and Validation of the Uncertainty Management in Problem-Based Learning Scale in Postsecondary STEM Education

中学后 STEM 教育中基于问题的学习量表的不确定性管理的开发和验证

• 发表时间: 2023-04
• 期刊: Annual Meeting of the American Educational Research Association 2023.
• 作者: Park, Jongchan;Deng Yuli;Agrawal Garima;Techawitthayachinda Ratrapee;Chen Ying;Huang Dijiang;Liu Huan.
• 通讯作者: Liu Huan.

AISecKG: Knowledge Graph Dataset for Cybersecurity Education

AISecKG：网络安全教育知识图数据集

• 发表时间: 2023-03
• 期刊: AAAI-MAKE 2023: Challenges Requiring the Combination of Machine Learning 2023
• 作者: Agrawal; Garima
• 通讯作者: Garima

Problems of Problem-Based Learning: Exploring Meta-Agency in Problem-Based Cybersecurity Learning in College Education

基于问题的学习的问题：探索大学教育中基于问题的网络安全学习的元代理

• 发表时间: 2023-04
• 期刊: Annual Meeting of the American Educational Research Association 2023
• 作者: Park, Jongchan;Deng Yuli;Agrawal Garima;Techawitthayachinda Ratrapee;Chen Ying;Huang Dijiang;Liu Huan
• 通讯作者: Liu Huan

Problem- Based Cybersecurity Lab with Knowledge Graph as Guidance

以知识图为指导的基于问题的网络安全实验室

• DOI: 10.37965/jait.2022.0066
• 发表时间: 2022-02-13
• 期刊: Journal of Artificial Intelligence and Technology
• 作者: Yuli Deng;Zhengping Zeng;Kritshekhar Jha;Dijiang Huang
• 通讯作者: Dijiang Huang