Optimizing and Securing Open RAN With Experimental System Validation
Kaydedildi:
| Yayımlandı: | ProQuest Dissertations and Theses (2025) |
|---|---|
| Yazar: | |
| Baskı/Yayın Bilgisi: |
ProQuest Dissertations & Theses
|
| Konular: | |
| Online Erişim: | Citation/Abstract Full Text - PDF |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|
| Özet: | 5G and beyond cellular networks promise remarkable advancements in bandwidth, latency, and connectivity, with the emergence of Open Radio Access Network (Open RAN) representing a pivotal direction. O-RAN inherently supports machine learning (ML) for dynamic network control, offering significant flexibility to reach competing goals through network slicing and closed-loop RAN control. To enable this, realistic and robust datasets are crucial for developing ML models. I collect a comprehensive 5G dataset using real-world cell phones across diverse scenarios, capturing traffic from various locations and mobility patterns. The traffic is replicated within a full-stack srsRAN-based O-RAN framework on Colosseum, the world's largest radio frequency (RF) emulator. The process produces a robust, O-RAN compliant KPI dataset reflecting real-world conditions. The framework facilitates ML model training for traffic slice classification and RAN optimization. These models achieve high accuracy, reaching up to 99% offline average accuracy and 92% online accuracy for specific slices. Building upon this robust dataset, I developed a physical resource block (PRB) assignment optimization strategy utilizing reinforcement learning that achieves a higher mean performance score (0.631) compared to expert (0.609) and random (0.588) policies. The strategy also reduces variability, ensuring consistent performance across user configurations. The O-RAN paradigm introduces cloud-based, multi-vendor, open, and intelligent architectures, enhancing network observability and reconfigurability. However, this also expands the threat surface, exposing components and ML infrastructure to cyberattacks. I examine O-RAN security, focusing on specifications, architectures, and intelligence proposed by the O-RAN Alliance. I identify threats, propose solutions, and experimentally demonstrate their effectiveness in defending O-RAN systems against cyberattacks, offering a holistic and practical perspective on O-RAN security. I investigate the impact of encryption on two key O-RAN interfaces: the E2 interface and the Open Fronthaul, using a full-stack O-RAN ALLIANCE compliant implementation within the Colosseum network emulator and a production-ready Open RAN and 5G-compliant private cellular network. Our findings provide quantitative insights into the latency and throughput impacts of encryption protocols, and I propose four fundamental principles for security by design within Open RAN systems. Finally, I address the security of Time-Sensitive Networking (TSN) in O-RAN. The O-RAN framework encourages multi-vendor solutions but increases the exposure of the open fronthaul (FH) to security risks, especially when deployed over third-party networks. Synchronization is crucial for reliable 5G links, with attacks on synchronization mechanisms posing significant threats. I demonstrate the impact of spoofing and replay attacks on Precision Time Protocol (PTP) synchronization, causing catastrophic failures in a production-ready O-RAN and 5G-compliant private cellular network. To counter these threats, I design an ML-based monitoring solution detecting various malicious attacks with over 97.5% accuracy, and outline additional security measures for the O-RAN environment. |
|---|---|
| ISBN: | 9798314844656 |
| Kaynak: | ProQuest Dissertations & Theses Global |