Adaptive Anomaly Detection and Congestion Avoidance for Large Scale 5G-IIoT: U-Plane Unsupervised Online Learning in RIC

The large-scale deployment of Internet of Things (IoT) devices and the increasing traffic volume bring congestion-related challenges to IoT systems. These challenges are particularly significant in large-scale Industrial IoT (IIoT) environments, where congestion caused by attacks or malfunctions threatens reliability. However, existing methods suffer from the following issues: 1) Control plane (C-Plane) key performance indicators (KPI)-based congestion avoidance methods cannot accurately classify devices based solely on C-Plane features. 2) Existing User Plane (U-Plane) empowered anomaly detection methods are mainly based on supervised learning and rely heavily on labeled datasets. To solve the problems, we propose an anomaly detection and congestion solution system based on U-Plane traffic data and Radio access network intelligent controller (RIC). First, we propose an unsupervised online learning-based anomaly traffic detection and congestion avoidance framework, where U-plane data is utilized to provide accurate classification. Second, we propose an anomaly device isolation mechanism from the main network by steering traffic using a near real-time RIC application (xAPP). Finally, we establish the simulation and hardware experimental platform to evaluate the effectiveness of our proposed system. In the experiment, the proposed method achieves a 94% classification accuracy, while the accuracy of the traditional method is 63%.