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

点击翻译按钮获取中文摘要

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.