IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i7p226-d1423771.html
   My bibliography  Save this article

Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies

Author

Listed:
  • José Cunha

    (Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain)

  • Pedro Ferreira

    (Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain)

  • Eva M. Castro

    (Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain
    Algoritmi Center, University of Minho, 4710-057 Braga, Portugal
    Department of Information Systems, School of Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal)

  • Paula Cristina Oliveira

    (Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal)

  • Maria João Nicolau

    (Algoritmi Center, University of Minho, 4710-057 Braga, Portugal
    Department of Information Systems, School of Engineering, University of Minho, Campus de Azurém, 4800-058 Guimarães, Portugal)

  • Iván Núñez

    (Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain)

  • Xosé Ramon Sousa

    (Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain)

  • Carlos Serôdio

    (Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
    Algoritmi Center, University of Minho, 4710-057 Braga, Portugal)

Abstract

The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies—Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)—in crafting advanced security solutions tailored for network slicing. AI’s predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks.

Suggested Citation

  • José Cunha & Pedro Ferreira & Eva M. Castro & Paula Cristina Oliveira & Maria João Nicolau & Iván Núñez & Xosé Ramon Sousa & Carlos Serôdio, 2024. "Enhancing Network Slicing Security: Machine Learning, Software-Defined Networking, and Network Functions Virtualization-Driven Strategies," Future Internet, MDPI, vol. 16(7), pages 1-36, June.
  • Handle: RePEc:gam:jftint:v:16:y:2024:i:7:p:226-:d:1423771
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/7/226/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/16/7/226/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:7:p:226-:d:1423771. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.