EAGER: SaTC-EDU: Cybersecurity Education in the Age of Artificial Intelligence: A Novel Proactive and Collaborative Learning Paradigm

人工智能(AI)技术，特别是机器学习(ML)，在提高生活质量方面显示出巨大的前景。然而，最近的研究表明，人工智能技术可能会被操纵、规避和误导。虽然在更好地理解人工智能技术的可信度和安全性方面取得了进展，但在将这些知识转化为教育和培训方面几乎没有采取什么行动。迫切需要培养一支合格的网络安全队伍，了解人工智能技术在网络安全领域的有用性、局限性和最佳做法。该项目将通过设计和实施一种虚拟的、主动的和协作的学习范例来解决这一重要问题，该范例可以吸引不同背景的学习者参与。这一方法将使广泛的学习者受益，特别是代表不足的学生。它还将帮助普通公众了解人工智能的安全影响。该项目有能力改变网络安全和AI/ML交叉点的教育；阐明网络安全中的可解释人工智能；并培养一支拥有人工智能能力的网络安全劳动力队伍。产品，包括研究结果和课程，将通过各种机制传播，如研讨会、同行评议会议和期刊。该项目通过组建一个在网络安全、人工智能和统计学方面具有专业知识的多学科团队来建设研究和教育能力。该团队将系统地调查两个具有凝聚力的研究和教育目标。首先，将开发沉浸式学习环境，通过构建具有有形对象的学习模型，激励学生在现实世界网络安全场景中探索AI/ML开发。拟议的学习环境实现了一种AI/ML机制，该机制将通过考虑个别学习者的不同背景知识来对AI/ML输出提供个性化解释。其次，该团队将设计一个积极主动的教育范式，鼓励学生协作识别网络安全领域中特定于AI/ML的新威胁，并开发创新和值得信赖的AI/ML解决方案。学习范式最终将使多学科人工智能-网络安全知识得以有效保留和转移。该项目由安全和值得信赖的网络空间(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以前从未探索过的新合作。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Artificial intelligence (AI) techniques, especially machine learning (ML), show great promise for improving quality of life. However, recent research has demonstrated that AI techniques can be manipulated, evaded, and misled. While progress has been made to better understand the trustworthiness and security of AI techniques, little has been done to translate this knowledge to education and training. There is a critical need to foster a qualified cybersecurity workforce that understands the usefulness, limitations, and best practices of AI technologies in the cybersecurity domain. This project will address this important issue by designing and implementing a virtual, proactive, and collaborative learning paradigm that can engage learners with different backgrounds. The approach will benefit a wide range of learners, especially underrepresented students. It will also help the general public understand the security implications of AI. This project has the ability to transform education at the intersection of cybersecurity and AI/ML; shed light on explainable AI in cybersecurity; and grow a cybersecurity workforce that possesses AI competencies. Products, including the research findings and curriculum, will be disseminated through a variety of mechanisms, such as workshops, peer-reviewed conferences, and journals. This project builds research and education capacity through the formation of a multidisciplinary team with expertise in cybersecurity, AI, and statistics. The team will systematically investigate two cohesive research and education goals. First, an immersive learning environment will be developed to motivate students to explore AI/ML development in the context of real-world cybersecurity scenarios by constructing learning models with tangible objects. The proposed learning environment enables an AI/ML mechanism that will provide personalized explanations on the AI/ML outputs by considering the distinct background knowledge of the individual learners. Second, the team will design a proactive education paradigm encourages students to collaboratively identify new AI/ML-specific threats in the cybersecurity domain and develop innovative and trustworthy AI/ML solutions. The learning paradigm will ultimately enable effective retention and transfer of multidisciplinary AI-cybersecurity knowledge.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.