论文标题
3D网络的多媒体流的联合半监督分类
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks
论文作者
论文摘要
随着当前对运输信息的加密趋势（例如，在HTTP加密的隧道之后）阻止中间节点访问端到端数据包标头，因此自动交通分类越来越重要。但是，此信息对于交通构建，网络切片和服务质量（QOS）管理至关重要，以防止网络入侵和异常检测。 3D网络提供多种可以保证不同级别QoS的路线。因此，服务分类和分离对于确保通过适当的网络中型的每个流量子流量确保所需的QoS级别至关重要。在本文中，提出了一种联合特征选择和减少功能学习计划，以半监督合作的方式对网络流量进行分类。 3D网络的联合网关有助于增强网络流量的全球知识，以提高异常的准确性以及新的流量的入侵检测和服务识别。
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.
