Security Challenges in 5G Network: A technical features survey and analysis

In this chapter, we present a comprehensive end-to-end (E2E) network slicing framework for fifth-generation (5G) wireless and core networks and provide our vision on network slicing for beyond 5G (B5G) networks using artificial intelligence (AI) technologies. We first give an introduction on the 5G communication networks by specifying distinct 5G networking characteristics. As the key features of 5G pose challenges on quality-of-service (QoS)-oriented resource provisioning, a new concept of network slicing is presented, based on software-defined networking (SDN) and network function virtualization (NFV) technologies, to enhance the overall resource utilization while guaranteeing QoS isolation among services. We focus on developing specific network slicing solutions for both 5G wireless networks and core networks. In addition, the evolution of B5G networks is discussed in terms of a potential networking paradigm shift with new service key performance indicators (KPIs). To satisfy B5G service requirements, a framework of AI-assisted network slicing lifecycle is developed to automate the slice creation with reduced slice management complexity. A case study is presented to demonstrate the effectiveness of the proposed E2E network slicing solutions.Keywords5G communication networksNetwork slicingWireless networksRadio resource slicingCore networksComputing and transmission resource slicingBeyond 5G networksAI-assisted network slicingQoSE2E delay

5G networks represent a paradigm shift from their 4G predecessors, engineered to cater to the burgeoning demands of densely populated areas and the high data throughput requirements of the Internet of Things (IoT) and smart city infrastructures. Beyond enhancing fundamental network attributes such as reliability, availability, and security, 5G introduces architectural innovations to accommodate these advanced needs. This paper delves into the architectural nuances of 5G, focusing primarily on the Standalone (SA) deployment method, distinguished by its utilization of network slicing. This approach leverages cutting-edge virtual network technologies, including Software-Defined Networking (SDN) and Network Function Virtualization (NFV), to forge virtual network slices tailored to diverse consumer needs. Such customization is imperative for meeting the expansive and varied connectivity demands characteristic of 5G networks.To navigate the complexities of 5G architecture, this study advocates for the adoption of Reference Architectures (RAs), which provide a high-level abstraction framework for dissecting and understanding intricate systems. By proposing a model that outlines the standalone architecture of 5G networks, our research lays the groundwork for evolving this model into a comprehensive RA for 5G. This foundational work is instrumental in equipping system designers with the tools necessary for generalizing systems to manage design and deployment challenges effectively, as well as the heterogeneity inherent in system components. Additionally, our proposed model facilitates the establishment of a common vocabulary, promoting a coherent understanding of component interactions and offering a unified system perspective. Ultimately, this paper contributes a pivotal model that not only enhances our comprehension of 5G network architecture but also paves the way for future advancements in network design and deployment, promising to significantly impact the development of smart cities and IoT ecosystems.Keywords: 5G, 5G architecture, Reference architecture, patterns. Network slicing, Network Function Virtualization, Virtual Network Function.

The fifth generation (5G) networks are expected to support diverse business verticals (i.e., manufacturing, health care, etc.) with varying quality of service requirements. While today’s mobile networks are a one size fits all architecture, tomorrow’s 5G mobile networks are envisioned to encourage agility, programmability and elasticity through enabling a software-based architecture promoted by network slicing. Network slicing is a new paradigm consisting of partitioning the underlying network infrastructure into different logical network slices, each dedicated to address the requirements (i.e., ultra-low latency, ultra-reliability, etc.) of a group of services. Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies have been identified as main enablers of network slicing, facilitating the fulfillment of the aforementioned services’ requirements. In this paper, we study the Latency-Aware service scheduling (LASS) problem to solve the network function mapping, the traffic routing and the network service scheduling in the context of an ultra-low latency network slice to consider services with stringent deadlines. We propose the LASS-Game, a novel game-theoretic approach presenting a scalable solution for the LASS problem that accounts for the centralized aspect of the problem while leveraging a decentralized mapping, routing and scheduling decisions.

Network slicing is a promising approach to meet the diverse requirements of the various use cases in the 5G networks. Hence, the mobile operators are moving forward to leveraging network slicing in order to measure up with the individual service expectations in their networks. Deploying different network slice types requires the global view of the network and the automated orchestration and management of the underlying resources. This is facilitated by utilizing software-defined networking and network function virtualization as the 5G key-enabler technologies. In this paper, we propose a practical network slicing resource management scheme which is comprised of a dynamic, priority-based resource allocation cooperating with an admission control unit. Adopting the proposed dynamic resource allocation would allow the admission control to comply with more NS requests while ensuring the desired requirements of the existing network slices. To validate the effectiveness of such a mechanism in a real environment, we take advantage of the features provided by OpenAirInterface and FlexRAN to efficiently manage multiple isolated network slices. In particular, we evaluate the significance of the network slicing, the isolation degree among created slices, and the effectiveness of the proposed scheme through several practical scenarios.

In 5G networks, the deployment of network slices enabled by Software-Defined Networking (SDN) is becoming a critical component for delivering tailored services to meet diverse application needs. However, this introduces challenges in network management, particularly in efficiently allocating resources to ensure that each network slice meets its specific Quality of Service (QoS) and availability requirements. Simultaneously, it must optimize overall network performance and network operator’s profit, which is linked to the Quality of Experience (QoE) of the end-users. Existing works offer either an availability-based solution or a QoE-aware solution to this problem, but not both. This paper addresses the end-to-end network slice resource allocation problem by simultaneously considering QoS and availability requirements in slice placement, while employing a QoE-aware strategy for resource allocation. We propose a framework that optimizes the network operator profit i.e. the highest QoE with the least resource usage, and can be flexibly configured to model realistic scenarios. Arbitrary network slice requirements can be defined using slice-specific QoS/QoE mapping, resource requirements, end-to-end latency and availability. For solving the formulated problem a Mixed Integer Nonlinear Programming (MINLP) formulation and efficient heuristic methods are proposed. Our solution accounts for the non-linear QoS/QoE relationship, utilizes redundantly placed Service Function Chains (SFCs) to increase availability, and supports the sharing of Virtual Network Functions (VNFs) among SFCs to optimize resource usage. Through extensive simulations on realistic network topologies and slice requests, we demonstrate the framework’s effectiveness in offering flexible and efficient network slice placement and resource allocation, utilizing a baseline heuristic from related studies. The results indicate that while the exact method delivers an optimal solution, heuristic approaches are suitable for time-sensitive tasks, such as dynamic slice configuration.

In order to meet the traffic demand from diverse next generation wireless network applications and exponentially increasing mobile subscriptions, various 5G network architectures are proposed by leveraging Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies. Network slicing will be one of the 5G technologies that would support next-generation wireless applications over a shared network infrastructure. However, improper network slicing may lead to either over-provisioning or under-utilization of the underlying network infrastructure resources, especially the 5G core network. Over-provisioning of data plane components such as Serving Gateway (SGW) and Packet Data Network Gateway (PGW) can lead to higher CAPEX and OPEX to mobile operators. In this paper, we propose a novel auto-scaling approach called Bit rate Aware Auto Scaling (BAAS) that maintains a precise UE bit rate requirement in the network slices without over-provisioning of data plane resources.

Network slicing is a key technology in fifth-generation (5G) mobile networks. Slicing divides a physical network into multiple dedicated logical networks to meet the requirements of diverse use cases. Efficient slice deployment algorithms are critical in reducing network operators' costs and energy consumption and in providing users better service. Many researchers have focused on static deployment when investigating network slices, effectively ignoring network operators' requirements for the dynamic deployment and expansion of such slices. In this paper, we first construct a joint optimization problem of cost and energy consumption. Then, we propose a prediction-assisted adaptive network slice expansion algorithm to deploy network slices dynamically. The proposed algorithm consists of three parts. First, we devise a Holt-Winters (HW) prediction algorithm to determine traffic demand for network slices. This method is intended to avoid frequent changes in network topology. Second, we propose a virtual network function (VNF) adaptive scaling strategy to reasonably determine the number of VNFs and resources required for network slices to avoid resource wastage. Finally, we develop a proactive online algorithm to deploy network slices. This method deploys network slices reasonably via the VNF deployment algorithm and link-routing algorithm to ensure slices' service requirements. Resource capacity and delay requirements are also considered in our evaluation to ensure that network costs and energy consumption are minimized. We then perform a series of simulation experiments to compare the proposed method's performance to state-of-the-art dynamic network slicing technologies. Ultimately, our solution is deemed a suitable candidate for dynamic deployment of 5G network slices; the solution demonstrates advantages of high resource utilization, low deployment costs, and low energy consumption.

The support of stringent requirements such as ultra-low latency and ultra-reliability of the forthcoming 5G services poses several challenges to telecommunications infrastructure providers. Network Function Virtualization, multi-access edge computing (MEC), and network slicing capabilities can help the support of such requirements. However, a trade-off between the cost of resource deployment and the support of service requirements needs to be taken into account in the design of NFV-based 5G networks. In this paper, we investigate the MEC location problem, which aims at selecting locations to place MECs hosting protected slices. We propose a MEC location problem enhanced with 1: 1 and 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> protection schemes for the provisioning of protected slices. In the 1: 1 scheme, protection is assured by reserving a backup slice for each tenant, whereas in the 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> scheme, a backup slice is shared among <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> tenants. The problem is modeled as a multi-criteria optimization problem and solved by the employment of a multi-objective evolutionary non-dominated sorting genetic algorithm. A comparison between the 1: 1 and 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> protection schemes is carried out in the context of 5G network slicing. Results show that the protection scheme 1: 1 can reduce the response time, at a higher deployment cost when compared to the 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> scheme.

In this Modern era, Software Defined Network (SDN), Network Function Virtualization (NFV), and cloud computing participating of Fifth Generation (5G) network emergence. This paper presents a robust security scheme to provide fortification against major threats along with user privacy in 5G network, two additional entities are introduced. For mobile users, initial authentication is provided at access points by an inventive Highly Secured Authentication and Handover Mechanism (HS-AOHM) scheme which minimizes handover latency without loss of user privacy. Then the authorized user packets are arrived at dispatcher in which a novel Tree Based Switch Assignment (TBSA) algorithm is incorporated. TBSA mitigates the flow table overloading attack by assigning packets to underloaded switches. In controller, DDoS attack is detected with the assist of entropy analysis. Then the suspicious packets are redirected to scrubbing Virtual Network Function (sVNF) in cloud. In sVNF, suspicious packets are classified into normal packets and malicious packets by using Hybrid Fuzzy with Artificial Neural Network (HF-ANN) classifier based on packet features. Normal packets are allowed to access applications whereas malicious packets are dropped at sVNF. Extensive simulation shows security improvement in 5G network in terms of handover latency, holding time, switch failure rate, detection accuracy, and delay.

Network slicing is an architectural solution that enables the future 5G network to offer a high data traffic capacity and efficient network connectivity. Moreover, software defined network (SDN) and network functions virtualization (NFV) empower this architecture to visualize the physical network resources. The network slicing identified as a multiple logical network, where each network slice dedicates as an end-to-end network and works independently with other slices on a common physical network resources. Most user devices have more than one smart wireless interfaces to connect to different radio access technologies (RATs) such as WiFi and LTE, thereby network operators utilize this facility to offload mobile data traffic. Therefore, it is important to enable a network slicing to manage different RATs on the same logical network as a way to mitigate the spectrum scarcity problem and enables a slice to control its users' mobility across different access networks. In this paper, we propose a mobility management architecture based network slicing where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In this architecture, each slice has a different mobility demands and these demands are governed by a network slice configuration and service characteristics. Therefore, our mobility management architecture follows a modular approach where each slice has individual module to handle the mobility demands and enforce the slice policy for mobility management. The advantages of applying our proposed architecture include: i) Sharing network resources between different network slices; ii) creating logical platform to unify different RATs resources and allowing all slices to share them; iii) satisfying slice mobility demands.

Network slicing is one of the most promising enabling technologies for next generation mobile network (5G), which spans over all domains of the network, including data centers, core networks (CNs), transport networks, and radio access networks (RANs). As such wide range of technologies are involved, it is difficult to ensure an end-to-end (network) slice working stably all the time. First, for both CN and RAN slices, bugs may accidentally occur in virtual network functions(VNFs), which can invalidate the slice and trigger a recovery process. In addition, the traffic demands to be accommodated by a CN or RAN slice can be stochastic rather than deterministic parameters, and changes of traffic demands that are too drastic may trigger slice reconfiguration. In this chapter, we investigate a robust RAN slicing mechanism to address slice recovery and reconfiguration in a unified framework. A slice is constituted by a set of VNFs and links and remapping is a process of re-selecting VNFs and links to recover failures. As a baseline, we first formulate a recovery problem to solve remapping of a slice with deterministic traffic demands. Based on that, we propose two robust RAN slicing algorithms for slice recovery under stochastic demands. Numerical results reveal that the proposed robust RAN slicing algorithms can provide adjustable tolerance of traffic uncertainties, compared with the baseline algorithm. In addition, the tradeoff between the robustness of slices and the average load of links and the tradeoff between the robustness and the average recovery time of slices can be efficiently managed and controlled.

Newly developed fifth- and sixth-generation (5G and 6G) networks are very secure, reliable, less latent, and versatile. These networks provide various services, like Massive Machine Type Communication (mMTC), Ultra-Reliable Low Latency Communication (URLLC), and Enhance Mobile Broadband (eMBB). Network slicing (NS) is important in both 5G and 6G networks as it enables the provision of the above services in a single physical network. This paper proposes an Optimal Hybrid Quantum-Classical Convolutional Neural Network for efficient 5G Network Slicing (HQCNN-5GNS). Initially, the input data are gathered from Unicauca IP flow version 2 and 5G network slicing dataset. The collected data are fed to the pre-processing phase using a Pseudo-linear Maximum Correntropy Kalman Filter (PMCKF) to remove duplicate data and replace the missing values from the dataset. The pre-processing information is provided to the Hybrid Quantum-Classical Convolutional Neural Network (HQCNN) to classify the network slicing into mMTC, eMBB, and URLLC. In general, HQCNN does not adopt any optimization methods to determine the optimal parameters to guarantee accurate categorization. Therefore, the Quantum Beetle Antennae Search Algorithm (QBASOA) is employed to optimize the weight parameter of HQCNN. The experimental outcomes show that the proposed HQCNN-5GNS attains 17.25%, 15.65%, and 19.55% higher accuracy than the existing approaches: Harris Hawks optimization depends on hybrid deep learning for effectual network slicing on 5G network (HHO-HDL-5GNS), Highly accurate with dependable wireless network slicing in the 5G network: a hybrid deep learning model (WNS-HDL-5GNS), Deep reinforcement learning-basis network slicing for beyond 5G (DRL-5GNS), respectively.

Network slicing is an emerging technology that is anticipated to realize the diverse QoS requirements required by the next generation (5G) networks. As key enablers of network slicing, software-defined network (SDN) and network function virtualization (NFV) has been fully researched and integrated into 5G architecture. In this paper, we propose a NFV MANO based network slicing framework for 5G networks. The proposed framework is a three-layered architecture using the MANO to control, manage and coordinate the slices' behavior.

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users.

Network availability and service continuity are major concerns for network operators to provide reliable communication services for Internet of Things (IoT), which are particularly challenging to achieve in virtualized network slicing environment where network services are exposed to the failure risks of both software (virtual network function (VNF) instances) and hardware (physical nodes). In general, the redundancy-based VNF backup solutions are used to improve the reliability of virtualized network slices. However, backup VNFs require the same amount of resources as the primary VNFs, which will result in high-resource cost. In this article, we propose a joint VNF partition and hybrid backup scheme for VNF orchestration, backup and mapping, whose aim is to construct the reliability-enhanced and delay-guaranteed network slices at minimum cost. Specifically, the VNF partition method divides a single VNF into multiple thinner VNFs with lower processing capacity and is expected to enhance the reliability of network slices with less additional resources. The hybrid backup scheme includes both onsite and offsite backup forms. Then, considering the time-varying network environment and IoT service requirements, we formulate the VNF orchestration, backup and mapping as a dynamic mixed integer linear programming (DMILP) problem, and model the dynamic problem as a Markov decision process (MDP). In view of the large action space of the formulated MDP, we propose a multiagent deep reinforcement learning (DRL) approach with an action space reduction strategy to achieve the dynamic VNF orchestration, backup and mapping solution. Simulation results demonstrate that the proposed joint VNF partition and hybrid backup scheme can obtain superior delay and reliability performance with low-network cost.