Open RAN Embracing Continual Learning: Towards NextG Adaptive Traffic Analysis

The future cellular networks, known as Next Generation (NextG), are anticipated to employ infrastructure based on cloud computing, with programmable, virtualized, and disaggregated designs. The separation of control functions from physical infrastructure will be implemented, and the utilization of standardized interfaces will facilitate the establishment of tailored closed-control loops. The O-RAN (Open Radio Access Network) paradigm aims to resolve the issue of limited control over the RAN by proposing an open design framework that enables data-driven and intelligent optimization at the individual user level. The growing ubiquity of O-RAN networks has underscored the significance of network traffic categorization in effectively managing O-RAN networks and preserving cybersecurity. This article analyzes the O-RAN alliance's disaggregated network architecture, focusing on its significant contribution to NextG networks. This paper proposes a novel approach to examine and assess traffic patterns inside the O-RAN architecture. The ongoing acquisition of knowledge regarding encrypted traffic is of utmost importance in light of the perpetual advancements in applications and the advent of encryption technologies. The ability to dynamically adjust in traffic analysis is crucial for effectively addressing the evolving network environment, specifically inside the O-RAN architecture. To address this, we propose a new method named Incremental O-RAN Traffic Categorization (IORTC), specially designed to categorize encrypted data within the O-RAN framework. The IORTC system manages the continuous collection of knowledge from encrypted traffic in the O-RAN framework. It is designed to adapt to the evolution of various encrypted traffic categories while retaining information about earlier encrypted traffic. Based on the experimental results, our method demonstrates a remarkable ability to assimilate new traffic patterns while managing the retention of previously acquired knowledge, and it performed well in all evaluation criteria with an average accuracy of 98%.