Optimizing and Securing Open RAN With Experimental System Validation
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| Veröffentlicht in: | ProQuest Dissertations and Theses (2025) |
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| 100 | 1 | |a Groen, Joshua | |
| 245 | 1 | |a Optimizing and Securing Open RAN With Experimental System Validation | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a 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. | |
| 653 | |a Electrical engineering | ||
| 653 | |a Computer engineering | ||
| 653 | |a Computer science | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3198970222/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3198970222/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |