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

社会越来越依赖于蜂窝网络，这使得确保网络免受网络攻击变得至关重要。下一代蜂窝网络预计将依靠机器学习（ML）算法来实现跨空间、时间、频率和设备的实时资源优化。该项目研究这些机器学习算法的安全威胁，并开发保护它们的解决方案，重点关注正在迅速普及的开放无线接入网络（Open RAN）架构。所有项目产出（算法、硬件/软件设计和数据集）将通过NSF RFDataFactory网站公开，帮助解决目前数据驱动无线研究缺乏大规模数据集的问题。作为该项目的一部分，几名研究生将在机器学习、安全、嵌入式系统和无线网络的交叉领域发展独特的专业知识。该项目的主要发现将整合到无线机器学习安全的新研究生课程中，并将丰富东北大学正在进行的针对来自代表性不足的少数群体的本科生和K-12学生的计划。研究了新的优化框架来模拟开放局域网中的对抗性机器学习攻击。这些发现被用于设计机器学习架构搜索算法，以找到开放ran的机器学习模型，这些模型在满足端到端延迟和能耗等限制的同时，对攻击具有弹性。该项目设计了异常检测技术，以增强动态设置中的弹性，以及针对实时数据集中毒攻击的动态防御策略。所提出的技术使用以下一个或多个测试平台进行评估：Colosseum网络模拟器、OpenRANGym框架和NSF PAWR POWDER平台。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。

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.