ML-Based 5G Network Slicing Security: A Comprehensive Survey

Ramraj Dangi, Akshay Jadhav, Gaurav Choudhary, Nicola Dragoni, Manas Kumar Mishra and Praveen Lalwani
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Ramraj Dangi: School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India
Akshay Jadhav: School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India
Gaurav Choudhary: DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
Nicola Dragoni: DTU Compute, Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark
Manas Kumar Mishra: School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India
Praveen Lalwani: School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India

Future Internet, 2022, vol. 14, issue 4, 1-28

Abstract: Fifth-generation networks efficiently support and fulfill the demands of mobile broadband and communication services. There has been a continuing advancement from 4G to 5G networks, with 5G mainly providing the three services of enhanced mobile broadband (eMBB), massive machine type communication (eMTC), and ultra-reliable low-latency services (URLLC). Since it is difficult to provide all of these services on a physical network, the 5G network is partitioned into multiple virtual networks called “slices”. These slices customize these unique services and enable the network to be reliable and fulfill the needs of its users. This phenomenon is called network slicing. Security is a critical concern in network slicing as adversaries have evolved to become more competent and often employ new attack strategies. This study focused on the security issues that arise during the network slice lifecycle. Machine learning and deep learning algorithm solutions were applied in the planning and design, construction and deployment, monitoring, fault detection, and security phases of the slices. This paper outlines the 5G network slicing concept, its layers and architectural framework, and the prevention of attacks, threats, and issues that represent how network slicing influences the 5G network. This paper also provides a comparison of existing surveys and maps out taxonomies to illustrate various machine learning solutions for different application parameters and network functions, along with significant contributions to the field.

Keywords: 5G network; network slicing; security; threats; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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