In an increasingly connected world, where reliance on cellular networks is paramount, ensuring their integrity is a concern. The WIoT Institute’s researchers are now working on the project “NeTS: Medium: Resilient-by-Design Data-Driven NextG Open Radio Access Networks”, which intersects machine learning with network security. This initiative, supported by NSF, aims to achieve real-time optimization for the next generation of cellular networks while thwarting potential cyber threats.

NeTS also analyzes security risks to machine learning algorithms and engineers’ solutions for protection, specifically focusing on the swiftly proliferating Open Radio Access Networks (Open RAN) architecture. It also covers formulating anomaly detection techniques, fortifying resilience within dynamic environments, and crafting dynamic defense strategies against real-time dataset poisoning attacks.

These innovative techniques undergo assessment through various testbeds, including the Colosseum network emulator, the OpenRANGym framework, and the NSF PAWR POWDER platform.

As an integral part of this initiative, several graduate students will develop unique expertise at the crossroads of machine learning, security, embedded systems, and wireless networks. These invaluable insights will be integrated into novel graduate courses in wireless ML security, amplifying ongoing endeavors at Northeastern University, particularly for underrepresented minority groups among undergraduate and K-12 students.

Project Abstract

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.

Source: NSF (https://www.nsf.gov/)