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

José Cunha (), Pedro Ferreira, Eva M. Castro, Paula Cristina Oliveira, Maria João Nicolau, Iván Núñez, Xosé Ramon Sousa and Carlos Serôdio ()
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José Cunha: Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Pedro Ferreira: Department of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Eva M. Castro: Optare Solutions, Parque Tecnológico de Vigo, 35315 Vigo, Spain
Paula Cristina Oliveira: Department of Engineering, School of Sciences and Technology, 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
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

Future Internet, 2024, vol. 16, issue 7, 1-36

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.

Keywords: network security; SDN; NFV; ML; network slicing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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