NeTS: Medium: Resilient-by-Design Data-Driven NextG Open Radio Access Networks

社会越来越依赖蜂窝网络，因此确保网络安全免受网络攻击至关重要。下一代蜂窝网络有望依靠机器学习(ML)算法来实现跨空间、时间、频率和设备的实时资源优化。该项目研究这些ML算法面临的安全威胁，并开发保护它们的解决方案，重点是正在迅速普及的开放式无线接入网络(Open RAN)体系结构。所有项目成果(算法、硬件/软件设计和数据集)将通过NSF RFDataFactory网站公开提供，帮助解决目前缺乏用于数据驱动的无线研究的大规模数据集的问题。作为该项目的一部分，几名研究生将在ML、安全、嵌入式系统和无线网络的十字路口开发独特的专业知识。该项目的主要发现将被整合到新的无线ML安全研究生课程中，并将丰富东北大学正在进行的针对来自未被充分代表的少数群体的本科生和K-12学生的倡议。研究了新的优化框架，以模拟Open RAN中的对抗性ML攻击。这些发现被用来设计ML体系结构搜索算法，以找到Open RAN的ML模型，这些模型在满足端到端延迟和能量消耗等约束的同时具有抗攻击能力。该项目设计了异常检测技术，以增强在动态环境下的弹性，以及针对实时数据集中毒攻击的动态防御策略。建议的技术使用以下一个或多个试验台进行评估：Colosseum网络仿真器、OpenRANGym框架和NSF PAWR粉末平台。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。

Society increasingly depends on cellular networks, making it critical to assure that the networks are secure against cyber attacks. Next-generation cellular networks are expected to rely on machine learning (ML) algorithms to achieve real-time resource optimization across space, time, frequency and devices. This project studies security threats to those ML algorithms and develops solutions to protect them, focusing on the Open Radio Access Networks (Open RAN) architecture which is rapidly becoming widespread. All project outputs (algorithms, hardware/software designs, and datasets) will be made publicly available through the NSF RFDataFactory website, helping to address the current lack of large-scale datasets for data-driven wireless research. As part of the project, several graduate students will develop unique expertise at the crossroads of ML, security, embedded systems and wireless networks. The project’s key findings will be integrated into new graduate courses in wireless ML security, and will enrich ongoing initiatives at Northeastern University for undergraduate and K-12 students coming from underrepresented minority groups.Novel optimization frameworks are investigated to model adversarial ML attacks in Open RANs. These findings are used to design ML architecture search algorithms to find ML models for Open RANs that are resilient to attack while still satisfying constraints such as end-to-end latency and energy consumption. The project designs anomaly detection techniques to enhance resilience in dynamic settings, and dynamic defense strategies against real-time dataset poisoning attacks. The proposed techniques are evaluated using one or more of the following testbeds: the Colosseum network emulator, the OpenRANGym framework, and the NSF PAWR POWDER platform.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.