NSF Convergence Accelerator Track G: Combating Vulnerability and Unawareness in 5G Network Security: Signaling and Full-Stack Approach

NSF 融合加速器轨道 G：对抗 5G 网络安全中的漏洞和无意识：信令和全栈方法

• 批准号: 2226447
• 负责人: Taejoon Kim
• 金额: $ 75万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2226447&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Combating

Over the last ten years, 5G research and network deployments have engendered significant economic development and greatly improved lives around the world. At the same time, the Department of Defense (DoD) has made significant efforts to leverage commercial investments made in 5G networks. The push for DoD to rely heavily on 5G commercial systems is, however, problematic because commercial networks are not designed for many of the adversarial settings and electronic warfare (EW) scenarios common in military-hardened networks. Academic research must play an important role in addressing fundamental security challenges arising from the vulnerabilities and design weaknesses of 5G networks. Such challenges manifest themselves in major threats that threaten confidentiality, integrity, and availability of 5G networks such as eavesdropping on messages, spoofing and man-in-the-middle attacks, distributed denial of service (DDoS), and downgrading the service from 5G to 3G/2G. Historically, however, many of the security-related and adversarial problems common to DoD have been viewed as strictly outside of the academic research purview. The proposed project aims to change this by building upon the momentum to accelerate academic and industry research into secure beyond-5G wireless networks. The team is joining forces from academia, industry, and government with the focus on consolidating the ongoing 5G security-related research efforts of its members. The project will also contribute to workforce development by creating research experiences, involving both theory and experiments, for a diverse team of both undergraduate and graduate students. The proposed research has three unique attributes that enable Zero Trust solutions: (a) Particular focus on signal/waveform level and 5G radio access network (RAN) security; (b) Fine-granular data-plane and control-plane threat detection, tracking, and defense mechanisms; and (c) Integration and evaluation via full-stack, Open RAN/Mobile Core testbed. DoD applications are the main motivation for the proposed solutions. To both narrow the scope of the efforts and make it more grounded, the proposed research will be organized across the following three interwoven aspects: (i) The modeling of threats at the user equipment (UE), RAN, Enhanced Data for Global Evolution (EDGE), backhaul, and 5G packet core levels to understand how suboptimal 5G networks are; (ii) The design of threat detection, tracking, and protection algorithms/mechanisms that effectively modify signaling at the 5G RAN and the software functions/protocols at the 5G Core for granular access control and encryption; and (iii) Formal verification of the various security requirements of service-based architecture in the context of 5G RAN, Core, and Internet Edge that use existing and novel programmable hardware. The level of visibility and controllability that this project enables would allow the 5G service-based architectures to adapt themselves quickly to make way for the military and other critical services in a secure and timely manner - similar to how cars make way for ambulances and fire trucks on the highways, sharing the same road infrastructure.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.

过去十年来，5G研究和网络部署带来了显着的经济发展，极大地改善了世界各地的生活。与此同时，国防部 (DoD) 为利用 5G 网络的商业投资做出了巨大努力。然而，推动国防部严重依赖 5G 商业系统是有问题的，因为商业网络并不是为军事强化网络中常见的许多对抗环境和电子战 (EW) 场景而设计的。学术研究必须在解决 5G 网络漏洞和设计缺陷带来的基本安全挑战方面发挥重要作用。这些挑战表现为威胁 5G 网络机密性、完整性和可用性的主要威胁，例如消息窃听、欺骗和中间人攻击、分布式拒绝服务 (DDoS) 以及将服务从 5G 降级到 5G 网络。 3G/2G。然而，从历史上看，国防部常见的许多与安全相关的对抗性问题一直被视为严格超出学术研究范围。拟议项目旨在通过加速学术和行业研究安全超 5G 无线网络的势头来改变这一现状。该团队正在联合学术界、工业界和政府，重点是巩固其成员正在进行的 5G 安全相关研究工作。该项目还将通过为本科生和研究生组成的多元化团队创造涉及理论和实验的研究经验，为劳动力发展做出贡献。 拟议的研究具有三个独特的属性，可实现零信任解决方案： (a) 特别关注信号/波形级别和 5G 无线接入网络 (RAN) 安全； (b) 细粒度数据平面和控制平面威胁检测、跟踪和防御机制； (c) 通过全栈、开放 RAN/移动核心测试台进行集成和评估。国防部应用是提出解决方案的主要动机。为了缩小工作范围并使其更加扎实，拟议的研究将分为以下三个相互交织的方面：（i）用户设备（UE）、RAN、全球演进增强数据的威胁建模（ EDGE）、回程和 5G 分组核心级别，以了解 5G 网络的次优程度； (ii) 威胁检测、跟踪和保护算法/机制的设计，可有效修改 5G RAN 的信令和 5G 核心的软件功能/协议，以实现精细的访问控制和加密； (iii) 在使用现有和新型可编程硬件的 5G RAN、核心和互联网边缘的背景下，对基于服务的架构的各种安全要求进行形式验证。该项目所实现的可见性和可控性水平将使基于 5G 服务的架构能够快速自我调整，以安全、及时的方式为军事和其他关键服务让路 - 类似于汽车为救护车和消防车让路在高速公路上，共享相同的道路基础设施。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

具有有限反馈码本的基于学习的自适应 IRS 控制

• DOI: 10.1109/twc.2022.3178055
• 发表时间: 2022-06
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Kim, Junghoon;Hosseinalipour, Seyyedali;Marcum, Andrew C.;Kim, Taejoon;Love, David J.;Brinton, Christopher G.
• 通讯作者: Brinton, Christopher G.

Dynamic and Robust Sensor Selection Strategies for Wireless Positioning With TOA/RSS Measurement

用于 TOA/RSS 测​​量无线定位的动态且鲁棒的传感器选择策略

• DOI: 10.1109/tvt.2023.3279833
• 发表时间: 2023-05
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Oh, Myeung Suk;Hosseinalipour, Seyyedali;Kim, Taejoon;Love, David J.;Krogmeier, James V.;Brinton, Christopher G.
• 通讯作者: Brinton, Christopher G.

Adaptive Frequency Hopping for 5G New Radio mMTC Security

用于 5G 新无线电 mMTC 安全的自适应跳频

• DOI: 10.1109/icit58465.2023.10143116
• 发表时间: 2023-04
• 期刊: 2023 IEEE International Conference on Industrial Technology (ICIT
• 作者: Chan, Wai Ming;Kwon, Hyuck M.;Chou, Rémi A.;Love, David J.;Fahmy, Sonia;Hussain, Syed Rafiul;Kim, Sang Wu;Valk, Chris Vander;Brinton, Christopher G.;Marojevic, Vuk;et al
• 通讯作者: et al

Time-Varying Noise Perturbation and Power Control for Differential-Privacy-Preserving Wireless Federated Learning

用于差分隐私保护无线联合学习的时变噪声扰动和功率控制

• 发表时间: 2023-11
• 期刊: and Computers
• 作者: Dang Qua Nguyen and Taejoon Kim