Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security
Omesh A. Fernando, Hannan Xiao, Joseph Spring
Abstract
Service providers have now entered the implementation phase for 5G mobile telecommunication networks. With this, the concept of Multi-access Edge Computing (MEC) will play a crucial role when providing services on-the-go with low latency, high availability and high bandwidth. However, due to the low processing power of MEC nodes, adversaries may target the platform for malevolent purposes. In this paper we focus on building a realistic 5G-MEC testbed to run legitimate traffic and network attacks, and to collect 5G datasets for 5G-MEC. We also apply a Convolutional Neural Network to the dataset created on our testbed and to publicly available datasets. Our datasets and detection rate suggest that the employment of current public datasets for research based on 5G-MEC security, is now inappropriate.