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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ramraj Dangi & Akshay Jadhav & Gaurav Choudhary & Nicola Dragoni & Manas Kumar Mishra & Praveen Lalwani, 2022. "ML-Based 5G Network Slicing Security: A Comprehensive Survey," Future Internet, MDPI, vol. 14(4), pages 1-28, April.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:4:p:116-:d:789792
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    References listed on IDEAS

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    1. Petar Radanliev & David Roure & Pete Burnap & Omar Santos, 2021. "Epistemological Equation for Analysing Uncontrollable States in Complex Systems: Quantifying Cyber Risks from the Internet of Things," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 381-411, November.
    2. Anabi Hilary Kelechi & Mohammed H. Alsharif & Athirah Mohd Ramly & Nor Fadzilah Abdullah & Rosdiadee Nordin, 2019. "The Four-C Framework for High Capacity Ultra-Low Latency in 5G Networks: A Review," Energies, MDPI, vol. 12(18), pages 1-35, September.
    3. Heejung Yu & Howon Lee & Hongbeom Jeon, 2017. "What is 5G? Emerging 5G Mobile Services and Network Requirements," Sustainability, MDPI, vol. 9(10), pages 1-22, October.
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    Cited by:

    1. Yves Adou & Ekaterina Markova & Yuliya Gaidamaka, 2022. "Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework," Future Internet, MDPI, vol. 14(10), pages 1-16, October.

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