EAGER: SaTC-EDU: Advancing Cybersecurity Education to Human-Level Artificial Intelligence

国家关键基础设施的所有部门都有望在日常运营中应用安全人工智能（AI），并受益于基于AI的决策工具和AI-human系统。这些结果需要对人类在不同环境中的行为有透彻的了解。然而，为了数学上的方便，现有的网络安全教育方法假设人类总是做出理性的决定。重要的因素，如混杂变量，往往被忽略。此外，强调学习进行相关和关联分析，对学习的因果分析重视不够。该项目将开发和评估教育模块，为新一代工程和计算机科学（CS）学生开发现实的决策计算模型做好准备。拟议的活动将推动网络安全教育从关联到因果分析，并有助于实现人类级别人工智能的目标。该项目将解决网络安全、隐私和人工智能教育方面的两个基本挑战。首先，该项目将调查如何工程和计算机科学的学生可以准备学习网络安全和隐私行为的计算。学生将学习应用先进的人工智能方法来开发解决情感和认知过程的决策的现实计算模型。其次，该项目将寻求了解如何开发网络安全和隐私中的因果（相对于相关）模型。项目团队将为学生提供发展因果网络与传统关联网络的机会。基于本研究的课程模块将在乔治亚理工学院现有的高级本科/研究生课程中实施和评估。该项目将评估这些模块对学生理解人工智能在解决网络安全和隐私问题方面的作用的影响。该项目由安全与可信网络空间（SaTC）计划的一项特别倡议支持，旨在促进网络安全、人工智能和教育领域之间前所未有的合作。SaTC项目与《联邦网络安全研究与发展战略计划》和《国家隐私研究战略》保持一致，旨在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。该奖项反映了美国国家科学基金会的法定使命，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。

All sectors of the national critical infrastructure are expected to apply secure artificial intelligence (AI) in their daily operations, and benefit from AI-based decision-making tools and AI-human systems. These outcomes require a thorough understanding of human behavior in different environments. However, for mathematical convenience, existing cybersecurity education approaches assume humans always make rational decisions. Important factors, such as confounding variables, are often ignored. In addition, there is an emphasis on learning to conduct correlation and association analyses, and insufficient attention paid to learning causation analysis. This project will develop and evaluate educational modules that will prepare a new generation of engineering and computer science (CS) students to develop realistic computational models of decision-making. The proposed activities will advance cybersecurity education from association to causation analysis and contribute to the goal of achieving human-level AI. This project will address two fundamental challenges in cybersecurity, privacy, and AI education. First, the project will investigate how engineering and CS students can be prepared to learn cybersecurity and privacy behaviors computationally. Students will learn to apply advanced AI methods to develop realistic computational models of decision-making that address both affective and cognitive processes. Second, the project will seek to understand how causal (vs. correlative) models in cybersecurity and privacy can be developed. The project team will provide opportunities for students to develop causal networks vs. traditional correlation networks. Course modules based on this research will be implemented and evaluated in existing advanced undergraduate/graduate courses at the Georgia Institute of Technology. The project will assess the impact of these modules on students' understanding on the role of AI in addressing cybersecurity and privacy issues. 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.