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

Synopsis

Our 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.

Personnel

Principal Investigator: Francesco Restuccia
Co-Principal Investigators: Alina Oprea, Tommaso Melodia
Senior Personnel: Salvatore D’Oro
Graduate Research Assistants: Tanzil Hassan, Andrea La Cava, Stefano Maxenti, Harsh Chaudhari, Giorgio Severi, Sazzad Sayyed, Shahriar Rifat

Publications

Meneghello, Francesca, Francesco Restuccia, and Michele Rossi. “WHACK: Adversarial Beamforming in MU-MIMO Through Compressed Feedback Poisoning.” IEEE Transactions on Wireless Communications, 2024.

Puligheddu, Corrado, Varshney, Nancy, Hassan, Tanzil, Ashdown, Jonathan, Restuccia, Francesco and Chiasserini, Carla Fabiana, “OffloaDNN: Shaping DNNs for Scalable Offloading of Computer Vision Tasks at the Edge”, IEEE 44th International Conference on Distributed Computing Systems (ICDCS), pp. 624-634, 2024.

Rifat, Shahriar and Ashdown, Jonathan and Restuccia, Francesco, “DARDA: Domain-Aware Real-Time Dynamic Neural Network Adaptation”, WACV 2025.

Rifat, Shahriar and De Lucia, Michael and Swami, Ananthram and Ashdown, Jonathan and Turck, Kurt and Restuccia, Francesco, “ADA: Adversarial Dynamic Test Time Adaptation in Radio Frequency Machine Learning Systems,” IEEE MILCOM 2024.

Zhang, Milin and Abdi, Mohammad and Rifat, Shahriar and Restuccia, Francesco, “Resilience of Entropy Model in Distributed Neural Networks,” ECCV 2024.

Zhang, Milin and De_Lucia, Michael and Swami, Ananthram and Ashdown, Jonathan and Turck, Kurt and Restuccia, Francesco, “HyperAdv: Dynamic Defense Against Adversarial Radio Frequency Machine Learning Systems,” IEEE MILCOM 2024.

Educational Activities and Outreach

This project is currently training several PhD students. These students are receiving rigorous training in adversarial machine learning and modern wireless technologies.  Moreover, Dr. Restuccia is actively involved in the UPLIFT and Young Scholars Program at Northeastern University for outreach to undergraduate researchers and K-12 students, respectively. In addition to preparing for research careers, these skills are increasingly in demand in major technology companies.

Dr. D’Oro has been invited to give a course on AI algorithms for O-RAN systems to members of the Taipei Computer Association (TCA). The course has a heavy focus on vulnerabilities of AI-based solutions in O-RAN, which includes adversarial learning and attacks.

Dr. Melodia and D’Oro have submitted a tutorial proposal to IEEE MILCOM 2024 to give a tutorial on these topics and to educate government agencies and contractors on security aspects related to O-RAN technologies based on AI. The tutorial has been accepted and will be given in late October 2024. Moreover, Dr. Melodia and D’Oro have given lectures at the 2024 PhD Summer School in Lipary (Italy), a summer school on networking aspects that attracts PhD students worldwide and is focused on developing the next-generation work force and train them on cutting-edge research topics.  

PI Restuccia is currently teaching a course named “Deep Learning and Edge Computing in Wireless Networks”. During the course, students present “AI in wireless” and “wireless for AI” papers, in addition to working on research assignments. As such, Restuccia is integrating the research conducted in this project with his educational efforts by (i) offering research-oriented  and development-oriented projects from the project; (ii) let the students present research papers published by the PI team on the topic covered by the project.

PI Oprea taught a seminar course on machine learning security and privacy in Fall 2023. The project has supported the development of adversarial machine learning modules for the class, which have been made publicly available through the course website. In Fall 2024, PI Oprea is teaching a seminar course on trustworthy generative AI for undergraduate and MS students that will also include modules on poisoning attacks and mitigations.

NSF Abstract: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312875&HistoricalAwards=false