Collaborative Research: EAGER SaTC-EDU: Artificial Intelligence and Cybersecurity: From Research to the Classroom

世纪最关键的安全挑战之一是保护管理和控制我们的基础设施、车辆、家庭和个人设备以及它们存储、使用和交换的信息的网络物理系统。 人工智能（AI）和基于机器学习的工具可以帮助人类分析师对大量数据进行分类，以确定是否发生了对这些系统的攻击。 然而，人工智能组件也容易受到攻击，需要开发技术使其更加强大。马里兰州巴尔的摩县大学（UMBC）和伊利诺伊大学之间的这个合作项目解决了人工智能和网络安全相结合的研究和教育方面的问题。将开发教育和培训材料，供学院和大学教师和学生以及网络安全和人工智能专业人员使用。这些材料将讨论人工智能如何改善安全系统，以及网络安全分析如何保护人工智能系统。此外，该项目将从传统上在计算领域代表性不足的群体中招收学生。该项目有三个相互关联的主题。第一个重点是教育，并扩展了项目团队现有的网络安全概念清单，以包括相关的人工智能相关概念。学生对网络安全和人工智能相关性的知识和理解将在参加人工智能或网络安全课程之前和之后进行评估。 还将创建教育材料和项目，以展示人工智能如何应用于网络安全问题，以及网络安全工具如何保护人工智能系统免受攻击。第二个主题探讨了最新的人工智能工具如何支持网络安全任务。网络威胁信息语义知识图的创建和维护将被研究并用于支持强化学习系统，这些系统更好地检测主机中恶意软件的存在。第三个主题侧重于寻找网络安全工具可以保护人工智能系统免受数据中毒等攻击的新方法。网络威胁知识图和神经网络将用于检测和消除用于训练基于人工智能的网络安全系统的数据中可能存在的虚假信息。该项目的这方面应用超出了网络安全，例如打击虚假信息。该项目得到了安全和值得信赖的网络空间（SaTC）计划的支持，该计划为解决网络安全和隐私问题的提案提供资金，在这种情况下，特别是网络安全教育。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。

One of the most critical security challenges of the 21st century is protecting the cyber-physical systems that manage and control our infrastructure, vehicles, homes, and personal devices as well as the information that they store, use and exchange. Artificial intelligence (AI) and machine learning-based tools can help human analysts sort through large volumes of data to determine if an attack on these systems has happened. Yet, AI components are also vulnerable to attacks, and require development of techniques to make them more robust. This collaborative project between the University of Maryland Baltimore County (UMBC) and the University of Illinois addresses the research and educational aspects of combining AI and cybersecurity. Educational and training materials will be developed for use by college and university instructors and students and by cybersecurity and AI professionals. These materials will address how AI can improve security systems and how cybersecurity analytics can protect AI systems. In addition, the project will recruit students from groups that have been traditionally underrepresented in computing. This project has three interrelated topics. The first focuses on education and extends the project team’s existing cybersecurity concept inventory to include relevant AI-related concepts. Student knowledge and understanding of cybersecurity and AI relatedness will be assessed before and after taking AI or cybersecurity courses. Educational materials and projects will also be created to demonstrate how AI can be applied to cybersecurity problems and how cybersecurity tools can protect AI systems from attack. The second topic explores how the latest AI tools can support cybersecurity tasks. The creation and maintenance of semantic knowledge graphs of cyberthreat information will be studied and used to support reinforcement learning systems that are better at detecting the presence of malware in a host. The third topic focuses on finding new ways that cybersecurity tools can protect AI systems from becoming compromised by attacks such as data poisoning. Cyberthreat knowledge graphs and neural networks will be used to detect and eliminate likely disinformation from data used to train AI-based cybersecurity systems. This aspect of the project has applications beyond cybersecurity, such as countering disinformation. This project is supported by the Secure and Trustworthy Cyberspace (SaTC) program, which funds proposals that address cybersecurity and privacy, and in this case specifically cybersecurity 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.