Dynamic Machine Learning Framework for Secure, Ultra-Reliable, and Energy-Efficient Open RAN Systems

Abstract

This paper proposes a machine learning (ML)-driven framework for Open Radio Access Networks (ORAN) to address security, Ultra-Reliable Low-Latency Communication (URLLC), and energy efficiency challenges. By integrating Isolation Forest for security, One-Class SVM for URLLC, and Generative Adversarial Networks (GANs) for cost optimization, the framework dynamically adapts to network traffic shifts while adhering to ORAN’s open architecture principles. Evaluations on a dataset of 72,000 instances with 21 features demonstrate 92% attack detection, 15% latency reduction, and 20% energy savings, validated through rigorous metrics such as precision-recall the area under the curve (AUC) and synthetic cost modeling. The proposed solution represents a significant advancement in the field of intelligent network management, offering a comprehensive approach to the most pressing challenges in modern telecommunications infrastructure. Through extensive simulations and real-world testing, we validate that our framework outperforms existing approaches in terms of attack detection accuracy, latency optimization, and energy efficiency without compromising the essential interoperability principles of the ORAN architecture.