University of Hertfordshire

By the same authors

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

Research output: Contribution to conferencePaperpeer-review

Standard

Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security. / Fernando, Omesh A. ; Xiao, Hannan; Spring, William Joseph.

2022. Paper presented at 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas, United States.

Research output: Contribution to conferencePaperpeer-review

Harvard

Fernando, OA, Xiao, H & Spring, WJ 2022, 'Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security', Paper presented at 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, United States, 10/04/22 - 13/04/22.

APA

Fernando, O. A., Xiao, H., & Spring, W. J. (Accepted/In press). Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security. Paper presented at 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas, United States.

Vancouver

Fernando OA, Xiao H, Spring WJ. Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security. 2022. Paper presented at 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas, United States.

Author

Fernando, Omesh A. ; Xiao, Hannan ; Spring, William Joseph. / Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security. Paper presented at 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, Texas, United States.6 p.

Bibtex

@conference{6513aabbed18471ca7a2940137c78579,
title = "Developing a Testbed with P4 to Generate Datasets for the Analysis of 5G-MEC Security",
abstract = "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 theplatform for malevolent tasks. Research at the edge is now taking place in both academia and industry, utilising publiclyavailable 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 apragmatic dataset that can be emploved in future 5G research. The components of our 5G-MEC tested include a homesubscriber server, a mobility management entity, a control and user plan separation gateway, a radio access network, an MECnode, user equipment connected via radio link and a Programming Protocol independent Packet Processing switch. Using ourtested 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 publiclv available datasetsused for detection. Our datasets and detection rate show that the employment of current public datasets for research based on5G-MEC security, is now inappropriate.",
author = "Fernando, {Omesh A.} and Hannan Xiao and Spring, {William Joseph}",
year = "2022",
language = "English",
note = "2022 IEEE Wireless Communications and Networking Conference (WCNC) : Emerging Technologies, Standards, and Applications, WCNC ; Conference date: 10-04-2022 Through 13-04-2022",

}

RIS

TY - CONF

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

Y1 - 2022

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 theplatform for malevolent tasks. Research at the edge is now taking place in both academia and industry, utilising publiclyavailable 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 apragmatic dataset that can be emploved in future 5G research. The components of our 5G-MEC tested include a homesubscriber server, a mobility management entity, a control and user plan separation gateway, a radio access network, an MECnode, user equipment connected via radio link and a Programming Protocol independent Packet Processing switch. Using ourtested 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 publiclv available datasetsused for detection. Our datasets and detection rate show that the employment of current public datasets for research based on5G-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 theplatform for malevolent tasks. Research at the edge is now taking place in both academia and industry, utilising publiclyavailable 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 apragmatic dataset that can be emploved in future 5G research. The components of our 5G-MEC tested include a homesubscriber server, a mobility management entity, a control and user plan separation gateway, a radio access network, an MECnode, user equipment connected via radio link and a Programming Protocol independent Packet Processing switch. Using ourtested 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 publiclv available datasetsused for detection. Our datasets and detection rate show that the employment of current public datasets for research based on5G-MEC security, is now inappropriate.

M3 - Paper

T2 - 2022 IEEE Wireless Communications and Networking Conference (WCNC)

Y2 - 10 April 2022 through 13 April 2022

ER -