Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks
This addresses traffic engineering challenges in encrypted 3D networks, though it appears incremental by applying federated learning to an existing domain.
The paper tackles the problem of classifying encrypted network traffic in 3D networks to enable traffic shaping and QoS management, proposing a federated semi-supervised learning scheme that enhances global knowledge for improved accuracy in anomaly detection and service identification.
Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semi-supervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.