Federated learning-based anomaly detection as an enabler for securing network and service management automation in beyond 5G networks

Abstract. Zero-touch network architecture (ZSM) is proposed to cater to unprecedented performance requirements, including network automation. 5G and beyond networks include exceptional latency, reliability, and bandwidth requirements. As a result, network automation is a necessity. ZSM architecture combines closed-loop mechanisms and artificial intelligence (AI) to meet the network automation requirement. Even though AI is prevalent, privacy concerns and resource limitations are growing concerns. However, techniques such as federated learning (FL) can be applied to address such issues. The proposed solution is a hierarchical anomaly detection mechanism based on the ZSM architecture, divided into domains by considering technical or business features. The network flow is categorized as an anomaly or not, and abnormal flows are removed from both stages. Detectors and aggregation servers are placed inside the network based on their purpose. The proposed detector is simulated with the UNSW-NB15 Dataset. The simulation results show accuracy improvement after the 2nd stage, and the detection accuracy varies with training data composition