Network Slicing in 5G and the Security Concerns

Mathew, Alex

doi:10.1109/iccmc48092.2020.iccmc-00014

Cited by 31 publications

(20 citation statements)

References 5 publications

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“…Network slicing allows rapid deployment of dedicated network slices with relatively effortless configurations over the public network. IoT botnets Remotely take control over the devices and perform some malfunctions Create a quarantine slice and move victimized devices into that slice and take necessary actions [53], [54] Zero-day attacks Exploits a serious software security weakness that the vendor or developer unaware of Identifying the vulnerable devices and move them to a quarantine slice until do necessary updates [53], [54] Man-in-The-Middle (MiTM) attacks…”

Section: B Improving Dynamicitymentioning

confidence: 99%

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Wijethilaka

Liyanage

2021

IEEE Commun. Surv. Tutorials

292

124

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Internet of Things (IoT) is an emerging technology that makes people's lives smart by conquering a plethora of diverse application and service areas. In near future, the fifthgeneration (5G) wireless networks provide the connectivity for this IoT ecosystem. It has been carefully designed to facilitate the exponential growth in the IoT field. Network slicing is one of the key technologies in the 5G architecture that has the ability to divide the physical network into multiple logical networks (i.e. slices) with different network characteristics. Therefore, network slicing is also a key enabler of realisation of IoT in 5G. Network slicing can satisfy the various networking demands by heterogeneous IoT applications via dedicated slices. In this survey, we present a comprehensive analysis of the exploitation of network slicing in IoT realisation. We discuss network slicing utilisation in different IoT application scenarios, along with the technical challenges that can be solved via network slicing. Furthermore, integration challenges and open research problems related to the network slicing in the IoT realisation are also discussed in this paper. Finally, we discuss the role of other emerging technologies and concepts, such as blockchain and Artificial Intelligence/Machine Learning(AI/ML) in network slicing and IoT integration.

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Section: B Improving Dynamicitymentioning

confidence: 99%

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Wijethilaka

Liyanage

2021

IEEE Commun. Surv. Tutorials

292

124

View full text Add to dashboard Cite

show abstract

“…This section covers surveys that elaborate on different security issues and how they have been managed. Mathew [89] illustrated the concept of network slicing and how it has gained traction in 5G. The author discussed the significant challenges in slicing, such as the security and implementation of RAN.…”

Section: Emerging Threats and Security Concerns In ML Based 5g Networ...mentioning

confidence: 99%

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

et al. 2022

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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.

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“…Interestingly, strict security and privacy requirements for sensitive IoT applications, such as I-health, can be maintained by exploiting the concept of slice isolation [126]- [128]. For instance, fog computing and network slicing have been used in [126] to develop a secure service-oriented authentication scheme for IoT systems over 5G network.…”

Section: Network Slicingmentioning

confidence: 99%

Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey

Abdellatif¹,

Mhaisen²,

Chkirbene³

et al. 2021

Preprint

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The rapid increase in the percentage of chronic disease patients along with the recent pandemic pose immediate threats on healthcare expenditure and elevate causes of death. This calls for transforming healthcare systems away from one-on-one patient treatment into intelligent health systems, to improve services, access and scalability, while reducing costs. Reinforcement Learning (RL) has witnessed an intrinsic breakthrough in solving a variety of complex problems for diverse applications and services. Thus, we conduct in this paper a comprehensive survey of the recent models and techniques of RL that have been developed/used for supporting Intelligent-healthcare (I-health) systems. This paper can guide the readers to deeply understand the state-of-theart regarding the use of RL in the context of I-health. Specifically, we first present an overview for the I-health systems challenges, architecture, and how RL can benefit these systems. We then review the background and mathematical modeling of different RL, Deep RL (DRL), and multiagent RL models. After that, we provide a deep literature review for the applications of RL in I-health systems. In particular, three main areas have been tackled, i.e., edge intelligence, smart core network, and dynamic treatment regimes. Finally, we highlight emerging challenges and outline future research directions in driving the future success of RL in I-health systems, which opens the door for exploring some interesting and unsolved problems.

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Network Slicing in 5G and the Security Concerns

Cited by 31 publications

References 5 publications

Survey on Network Slicing for Internet of Things Realization in 5G Networks

Survey on Network Slicing for Internet of Things Realization in 5G Networks

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

Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey

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