Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security

TY - CHAP

T1 - Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security

AU - Fernando, Omesh A.

AU - Xiao, Hannan

AU - Spring, William Joseph

PY - 2022/4

Y1 - 2022/4

N2 - Service providers have now entered the implementation phase of 5G mobile telecommunication networks. With this realisation the concept of Multi-access Edge Computing (MC) 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 tasks. Research at the edge is now taking place in both academia and industry, utilising publicly available datasets in the development of Intrusion Detection Systems and traffic analysis. Research though conducted on a 5G-MEC tested utilising realistic data is still lacking. In this paper we focus on building a realistic 5G-MEC tested to generate a pragmatic dataset that can be employed in future 5G research. The components of our 5G-MEC tested include a home subscriber server, a mobility management entity, a control and user plan separation gateway, a radio access network, an MEC node, user equipment connected via radio link and a Programming Protocol independent Packet Processing switch. Using our tested we have run legitimate traffic and network attacks and collected the associated data, generating datasets for 5G-MEC. We have also applied a Convolutional Neural Network to the dataset created on our testbed and to publicly available datasets used for detection. Our datasets and detection rate show that the employment of current public datasets for research based on 5G-MEC security, is now inappropriate.

AB - Service providers have now entered the implementation phase of 5G mobile telecommunication networks. With this realisation the concept of Multi-access Edge Computing (MC) 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 tasks. Research at the edge is now taking place in both academia and industry, utilising publicly available datasets in the development of Intrusion Detection Systems and traffic analysis. Research though conducted on a 5G-MEC tested utilising realistic data is still lacking. In this paper we focus on building a realistic 5G-MEC tested to generate a pragmatic dataset that can be employed in future 5G research. The components of our 5G-MEC tested include a home subscriber server, a mobility management entity, a control and user plan separation gateway, a radio access network, an MEC node, user equipment connected via radio link and a Programming Protocol independent Packet Processing switch. Using our tested we have run legitimate traffic and network attacks and collected the associated data, generating datasets for 5G-MEC. We have also applied a Convolutional Neural Network to the dataset created on our testbed and to publicly available datasets used for detection. Our datasets and detection rate show that the employment of current public datasets for research based on 5G-MEC security, is now inappropriate.

M3 - Conference paper

BT - 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, United States

PB - IEEE

ER -