Collaborative Research: SaTC: EDU: A Hands-on Approach to Securing Self-Driving Networks

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。今天的计算机网络在复杂性和规模上都在增长，部分原因是为了应对物联网、云3.0和大数据时代的各种连接需求。随着网络的日益复杂，现有的网络管理方案已经无法满足实时网络管理的需求。网络管理需要一种全新的方法，使网络能够自主地控制、配置和管理自己。虽然这些“自驾车网络”为有效的网络管理提供了许多机会，但它们也引入了新的威胁和攻击媒介，必须加以解决以确保其安全。不幸的是，目前缺乏经过自主和智能网络安全培训的专业人员，也缺乏培训这些专业人员的内容。该项目将通过开发实验室密集型模块来解决这些不足，使本科生能够获得确保下一代自动驾驶网络安全的基础和高级知识。项目团队将开发六个独立的模块，全面覆盖保护自动驾驶网络所需的技术、工具和方法。这些模块将涵盖(1)攻击调查和分析遗留，软件定义网络（SDN），和对抗性攻击；(2)利用静态分析、软件组件分析、自动化已知攻击模式和行为分析寻找漏洞；(3)使用经典和基于OpenFlow的方法进行全网数据收集；(4)保护采用运行时应用程序自我保护、自动修补、安全信息和事件管理；(5)采用持续自适应风险和推力评估方法进行评估；(6)使用安全编排、自动化和响应的操作。此外，该项目旨在通过组织研讨会和参加专业会议来增加代表性不足的群体对STEM的参与。项目小组还将组织一个讲习班，向其他组织感兴趣的教员分发这些模块。该项目由安全与可信网络空间（SaTC）计划支持，该计划资助解决网络安全和隐私问题的提案，在这种情况下，特别是网络安全教育。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Today’s computer networks have grown both in complexity and in scale, in part as a response to diverse connectivity requirements in the era of the Internet of Things, Cloud 3.0, and Big Data. Existing network management solutions cannot keep up with the demand for solutions to real-time network management problems resulting from these increasingly complex networks. Network management requires a fundamentally new approach in which networks can autonomously control, configure, and manage themselves. While these “self-driving networks” offer numerous opportunities for efficient network management, they introduce new threats and attack vectors that must be addressed to secure them. Unfortunately, there is currently a shortfall of professionals trained in autonomous and intelligent network security and a shortfall of content with which to train these professionals. This project will address these shortfalls by developing lab-intensive modules that enable undergraduate students to gain fundamental and advanced knowledge in securing next-generation self-driving networks. The project team will develop six self-contained modules with comprehensive coverage of the techniques, tools, and methods required to secure self-driving networks. The modules will cover (1) attack investigation and analysis of legacy, Software Defined Networking (SDN), and adversarial attacks; (2) hunting for vulnerabilities using static analysis, software component analysis, automated known attack patterns, and behavioral analysis; (3) network-wide data collection using classic and OpenFlow based approaches; (4) protection using runtime application self-protection, automated patching, and security information and event management; (5) evaluation using continuous adaptive risk and thrust assessment; and (6) action using security orchestration, automation and response. In addition, this project aims to increase the participation of underrepresented groups in STEM by organizing workshops and participating in professional conferences. The project team will also organize a workshop to disseminate the modules to interested faculty members from other organizations.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.