The field of Open Radio Access Network (O-RAN) is witnessing significant developments in security and optimization. Researchers are exploring innovative solutions to address the challenges introduced by the openness and disaggregation of mobile network architecture. One of the key areas of focus is the development of monitoring frameworks to enhance transparency and trust in O-RAN operations. Another important aspect is the application of machine learning techniques to detect anomalies and predict potential throughput drops, enabling proactive handover initiation and self-healing networks. Furthermore, the optimization of routing, resource allocation, and energy consumption in Integrated Access and Backhaul (IAB) networks is being investigated to accommodate the diverse requirements of next-generation networks. Noteworthy papers in this area include:

• Inter-DU Load Balancing in an Experimental Over-the-Air 5G Open Radio Access Network, which presents a fully open-source implementation of Load Balancing in an experimental 5G New Radio network.
• Closing the Visibility Gap: A Monitoring Framework for Verifiable Open RAN Operations, which proposes a monitoring framework for low-trust O-RAN environments to proactively verify configuration state and control behavior against tenant-defined policies.
• Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics, which develops an anomaly detection framework to proactively detect possible throughput drops and minimize post-handover failures.
• Joint Routing, Resource Allocation, and Energy Optimization for Integrated Access and Backhaul with Open RAN, which addresses the joint optimization of routing and resource allocation in IAB networks to meet performance requirements and reduce energy consumption.