Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

TL;DR

This paper proposes a federated semi-supervised learning scheme for classifying multimedia traffic flows in 3D networks, enhancing traffic management and security without compromising encryption.

Contribution

It introduces a novel federated feature selection and reduction method for semi-supervised traffic classification in 3D networks, improving accuracy and privacy.

Findings

Improved traffic classification accuracy in 3D networks.

Enhanced anomaly and intrusion detection capabilities.

Effective semi-supervised federated learning approach.

Abstract

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.