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

基本信息

  • 批准号:
    2114789
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 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

项目摘要

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零散的信息,无法为学习者提供如何连接和建立他们所学概念的适应性指导。该项目将开发一种支持人工智能(AI)的网络安全工具,称为知识图(AISecKG),以应对这一网络安全教育挑战。被搜索引擎和社交网络广泛使用的知识图整合了数据,并可以存储对象、概念和事件等项目的链接描述。该项目通过为大学生提供灵活的学习计划,提高他们的批判性思维和解决问题的能力,为网络安全教育应用了一种新的学习方法。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,为他们提供一个整体的观点,并更好地准备他们解决现实世界的复杂性。AISecKG的开发和部署是跨学科的。首先,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,以进行学习规划和学习成果评估。其次,它采用多层次的评估方法来设计网络安全课程,支持学生的认知参与,并提高学生的学习成果。AISecKG有两个主要的设计目标。首先,它将指导教师根据他们的学习目标开发以问题为基础的学习课程。其次,它将允许学生应用自适应学习策略,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)为基于问题的网络安全教育部署一个循证模型和学习材料,重点是在使用有针对性的材料和方法的同时发展教师的能力和实践;(B)为深度学习制定一个富有成效的教学模型,促进科学探究和设计的文化以及一套发展学生能力的战略;以及(c)提供学生学习成果的证据,作为教学资源,以支持学生在互动式学习任务中的认知参与。该项目得到了安全和值得信赖的网络空间(SaTC)计划的特别倡议的支持,以促进网络安全,人工智能和教育领域之间新的,以前未探索的合作。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。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey
  • DOI:
    10.48550/arxiv.2311.07914
  • 发表时间:
    2023-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Garima Agrawal;Tharindu Kumarage;Zeyad Alghami;Huanmin Liu
  • 通讯作者:
    Garima Agrawal;Tharindu Kumarage;Zeyad Alghami;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
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
AISecKG: Knowledge Graph Dataset for Cybersecurity Education
AISecKG:网络安全教育知识图数据集
Building Knowledge Graphs from Unstructured Texts: Applications and Impact Analyses in Cybersecurity Education
  • DOI:
    10.3390/info13110526
  • 发表时间:
    2022-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Garima Agrawal;Yuli Deng;Jongchan Park;Huanmin Liu;Yingying Chen
  • 通讯作者:
    Garima Agrawal;Yuli Deng;Jongchan Park;Huanmin Liu;Yingying Chen
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Huan Liu其他文献

Silica coating with well-defined micro-nano hierarchy for universal and stable surface superhydrophobicity
具有明确微纳米层次结构的二氧化硅涂层,具有通用且稳定的表面超疏水性
  • DOI:
    10.1016/j.cplett.2019.06.001
  • 发表时间:
    2019-09
  • 期刊:
  • 影响因子:
    2.8
  • 作者:
    Huan Liu;Wei Geng;Cheng-Jing Jin;Si-Ming Wu;Yi Lu;Jie Hu;Hao-Zheng Yu;Gang-Gang Chang;Tian Zhao;Ying Wan;Zhi-Qiang Luo;Ge Tian;Xiao-Yu Yang
  • 通讯作者:
    Xiao-Yu Yang
The Construction and Application of a Multipoint Sampling System for Vehicle Exhaust Plumes
汽车尾气多点采样系统的构建与应用
  • DOI:
    10.4209/aaqr.2017.02.0076
  • 发表时间:
    2017
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Xianbao Shen;Zhiliang Yao;Kebin He;Xinyue Cao;Huan Liu
  • 通讯作者:
    Huan Liu
MnO2/HF/HNO3/H2O System for High-Performance Texturization on Multi-Crystalline Silicon
用于多晶硅高性能织构化的 MnO2/HF/HNO3/H2O 系统
  • DOI:
    10.4028/www.scientific.net/msf.960.263
  • 发表时间:
    2019-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Huan Liu;Lei Zhao;Hongwei Diao;Wenjing Wang
  • 通讯作者:
    Wenjing Wang
Effect of spatial distribution of boron and oxygen concentration on DNA damage induced from boron neutron capture therapy using Monte Carlo simulations
使用蒙特卡罗模拟,硼和氧浓度的空间分布对硼中子俘获疗法引起的 DNA 损伤的影响
  • DOI:
    10.1080/09553002.2021.1928785
  • 发表时间:
    2021-05
  • 期刊:
  • 影响因子:
    2.6
  • 作者:
    Jie Qi;Changran Geng;Xiaobin Tang;Feng Tian;Yang Han;Huan Liu;Yuanhao Liu;Silva Bortolussi;Fada Guan
  • 通讯作者:
    Fada Guan
Multiauthority Attribute-Based Keyword Search over Cloud-Edge-End Collaboration in IoV
车联网云边端协作基于多权限属性的关键词搜索

Huan Liu的其他文献

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{{ truncateString('Huan Liu', 18)}}的其他基金

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
  • 财政年份:
    2024
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
III: SMALL: Graph Contrastive Learning for Few-Shot Node Classification
III:SMALL:少样本节点分类的图对比学习
  • 批准号:
    2229461
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
III: Small: Discovering and Characterizing Implicit Links in Graph Data
III:小:发现和表征图数据中的隐式链接
  • 批准号:
    1614576
  • 财政年份:
    2016
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
III: Small: Transforming Feature Selection to Harness the Power of Social Media
III:小:转变特征选择以利用社交媒体的力量
  • 批准号:
    1217466
  • 财政年份:
    2012
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
NSF Conference Sponsorship for the Third International Conference on Social Computing, Behavioral Modeling, and Prediction
NSF 会议赞助第三届社会计算、行为建模和预测国际会议
  • 批准号:
    1019597
  • 财政年份:
    2010
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
NSF Workshop Sponsorship for the Second International Workshop on Social Computing, Behavioral Modeling, and Prediction
NSF 研讨会赞助第二届社会计算、行为建模和预测国际研讨会
  • 批准号:
    0908506
  • 财政年份:
    2009
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
III-COR-Small: Beyond Feature Selection and Extraction - An Integrated Framework for High-Dimensional Data of Small Labeled Samples
III-COR-Small:超越特征选择和提取 - 小标记样本高维数据的集成框架
  • 批准号:
    0812551
  • 财政年份:
    2008
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
A Collaborative Project: Development of An Undergraduate Data Mining Course
合作项目:本科数据挖掘课程的开发
  • 批准号:
    0231448
  • 财政年份:
    2003
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
SGER: Toward a Unifying Taxonomy for Feature Selection
SGER:迈向特征选择的统一分类法
  • 批准号:
    0127815
  • 财政年份:
    2001
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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SaTC-EDU:EAGER:为高中生开发元宇宙原生安全和隐私课程
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  • 批准号:
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  • 批准号:
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