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Michael G. Xevgenis
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Lindsay Dong
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Xevgenis, M.; Kogias, D.G.; Karkazis, P.A.; Leligou, H.C. Addressing ZSM Security Issues with Blockchain Technology. Encyclopedia. Available online: https://encyclopedia.pub/entry/54026 (accessed on 19 May 2024).
Xevgenis M, Kogias DG, Karkazis PA, Leligou HC. Addressing ZSM Security Issues with Blockchain Technology. Encyclopedia. Available at: https://encyclopedia.pub/entry/54026. Accessed May 19, 2024.
Xevgenis, Michael, Dimitrios G. Kogias, Panagiotis A. Karkazis, Helen C. Leligou. "Addressing ZSM Security Issues with Blockchain Technology" Encyclopedia, https://encyclopedia.pub/entry/54026 (accessed May 19, 2024).
Xevgenis, M., Kogias, D.G., Karkazis, P.A., & Leligou, H.C. (2024, January 18). Addressing ZSM Security Issues with Blockchain Technology. In Encyclopedia. https://encyclopedia.pub/entry/54026
Xevgenis, Michael, et al. "Addressing ZSM Security Issues with Blockchain Technology." Encyclopedia. Web. 18 January, 2024.
Addressing ZSM Security Issues with Blockchain Technology
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This entry is adapted from the peer-reviewed paper 10.3390/fi15040129

Undoubtedly, we are witnessing a new era of computer networks that aspire to support modern demanding applications by providing the highest Quality of Experience (QoE) to the end user. Next Generations Networks (NGNs) ensure that characteristics such as ultra-low latency, high availability and wide service coverage can be met across the network regardless of the network infrastructure ownership. Blockchain technology inherently addresses security in trustless environments such as the infrastructures defined by the Zero-touch Service Management (ZSM) team.

next generation networks Blockchain ZSM

1. Introduction

Next Generation Networks (NGNs) offer network services based on technologies such as Software Defined Networking (SDN) and Network Function Virtualization (NFV). SDN and NFV reshape the nature of modern networks as they support network services via virtualized environments, without the need for a hardware networking device [1][2]. Therefore, the Providers (NPs) can easily trade (virtualized) resources to support modern Network Services (NSs) at different Quality of Service levels without having to resort to optimization of every single algorithm (from routing like [3] to the upper layer). In the new scene, the marketplace of resources grows as new players are entering the market [4][5][6]. These services are implemented by one or many Virtual Network Functions (VNFs) which are supported by a collection of computational resources in the form of Virtual Machines (VMs) or Containers (i.e., Dockers). The performance of the NSs both in terms of availability and latency affects the Quality of Experience (QoE) of the end-user [7][8]. Therefore, it is crucial to orchestrate the performance of the individual NSs [9][10]. Considering that modern networks must support applications with very different QoS requirements, the ability of flexible and agile provisioning of high availability, ultra-low latency and 100% coverage is of high importance [11][12] and SDN/NFV-enabled networks play a crucial role in this [13][14]. Massive, seemingly infinite capacity, imperceptible latency, ultra-high reliability, personalized services with extreme improvements in customer experience, global web-scale coverage, and support for massive machine-to-machine communication are only a subset of the requirements that these deployments should fulfill. The flexibility offered by the SDN/VNF architecture opens the opportunity for NPs to enhance the utilization of their resources by the dynamic reconfiguration and allocation of workload to the different devices/resources. For given NS demands, NPs can trade resources to support NSs with predefined network characteristics when needed. To maximize the benefits of resource sharing, NPs need a framework for a highly dynamic, self-optimized resource management process. Additionally, to avoid human errors and reduce the response time of the system, the resource management process should require minimum human intervention.
The requirements of NPs have led the research community to the development of the Zero-touch network and Service Management (ZSM) standardization, kicked off by the ETSI (the European Standards Organization) in 2017 [15]. The pivotal deployment of 5/6G and network slicing gave birth to the need for a radical change regarding the management and orchestration of modern networks and services. More specifically, there is a need to handle: (a) the increased overall complexity of networks derived from their transformation into programmable, software-driven, service-based and holistically managed architectures, (b) the unprecedented operational agility (i.e., real-time management of NS) required to support new business opportunities enabled by technology breakthroughs, such as network slicing. The ultimate automation goal is to enable largely autonomous networks which will be driven by high-level policies and rules; these networks will be capable of self-configuration, self-monitoring, self-healing and self-optimization without further human intervention.
Besides the important benefits stemming from ZSM introduction, a set of security challenges are also introduced in this highly dynamic, automated resource management environment, as already pointed out by their proposers [15]. The concerns are related to the untrusted nature of modern networks where large numbers of NPs are involved and the security level of the automated mechanisms (many times powered by Artificial Intelligence (AI) and Machine Learning (ML)). Since these automation mechanisms are responsible to make important decisions regarding the management of the network, the safeguarding of this mechanism is vital for the network’s well-being. These mechanisms must not be compromised, and their decisions must not be manipulated or tampered with. Although several solutions have been discussed in the ETSI documents, the complexity of the system increases as multiple different techniques are combined.
On the other hand, Blockchain technology (one of the most hyped technologies in 2022) is adopted in many different use cases. The ability to establish trust in an untrusted environment, the data integrity and the transaction validity ensured in the absence of a trusted third party are the main characteristics that make blockchain attractive. To accomplish that, blockchain solutions run in a decentralized and distributed network of nodes that are characterized as public, private, permissioned and permissionless, offering different degrees of participation control. In the last few years, blockchain has been successfully adopted in several sectors beyond cryptocurrency, such as supply chain management, maritime and gaming with several distributed applications (Dapps) [16][17][18][19][20].

2. Addressing ZSM Security Issues with Blockchain Technology

Authors in [21], present a combination of AI technology and DLTs in order to increase the security and trust in multi-operator mobile/cellular networks. The authors highlight the ability of AI to offer characteristics such as self-adaptation and self-reaction to next-generation networks which are susceptible to changes regarding the network conditions. This research is part of the 5GZORRO project, and its goal is to present a conceptual architecture of a solution that uses AI and DLTs. Another work of the same project [22] proposes the use of Smart Contracts (SCs) coupled with Cloud-Native operational Data Lakes to provide a zero-touch solution for the automated service assurance of multi-domain network slices. The SLAs which define the proper performance of the services are applied in the form of Smart Contracts (SCs) deployed in a blockchain network to increase the transparency of the process and to facilitate the integrity of the agreement. This research presents an architecture for a Smart Contract-based service assurance mechanism for network slices in a multi-domain environment that is SLA-driven.
Benzaid et al. [23], describe the concept of Zero Touch Networks (ZTNs) and how AI can be used to automate the service management of modern networks. However, beyond the advantages of AI-driven ZTNs, security and trust are considered open issues by the authors when AI is used. According to the authors, it has been proven that ML techniques are vulnerable to several attacks targeting both the training phase and the test phase. Since data are used by the AI mechanism, their integrity and provenance are important for the proper operation of the mechanism. Authors claim that blockchain technology can be the antidote to these security limitations, due to its immutability and distributed nature, without providing any architecture or details for the design of such a solution.
In [24], the authors discuss the considerations regarding trust in modern multi-stakeholder networks and propose the use of blockchain technology to deal with trust issues. Smart Contracts (SCs) deployed in blockchain networks are ideal to create Service Level Agreements (SLAs) among stakeholders and control SLA violations in a transparent and secure manner. Based on the table presented by the authors, blockchain can be combined with many other technologies to solve trust and security issues in modern networks. Some of these technologies are VNFs, AI and ML. Moreover, sensitive data in modern networks can be protected using blockchain technology in order to guarantee their integrity and provenance. The authors discuss a use case where data are used as fuel for AI and ML focusing on the importance of data security and highlighting that data security is extremely important in AI/ML-based solutions. Data must be untampered and protected in order to avoid dataset poisoning which may lead to wrong decisions taken by the AI and ML mechanisms. In this use case, the data can be relevant to the service deployment parameters and the measured quality while blockchain technology could solve the security and trust issues.
Liyanage et al. [25] present the progress of ZSM standardization and highlight the main goals and challenges. The security threats highlighted by the authors are ML/AI-based attacks, open API security threats, intent-based security threats, automated Closed-Loop network-based security threats, and threats due to programmable network technologies. Moreover, the multidomain and heterogeneous nature of modern networks labels trust among different entities as a major issue. According to the authors these open issues have not been sufficiently explored, although there are some published ideas where the use of blockchain is discussed as a solution.

References

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  3. García-Otero, M.; Zahariadis, T.; Alvarez, F.; Leligou, H.C.; Población-Hernández, A.; Karkazis, P.; Casajús-Quirós, F.J. Secure geographic routing in ad hoc and wireless sensor networks. EURASIP J. Wirel. Commun. Netw. 2010, 2010, 1–12.
  4. Unleashing a New Breed of 5G Services: A 2021 Ecosystem Makeover. Available online: https://www.forbes.com/sites/forbestechcouncil/2021/03/01/unleashing-a-new-breed-of-5g-services-a-2021-ecosystem-makeover/?sh=643358bc4c83 (accessed on 20 September 2022).
  5. How the Cloud Telecommunications Revolution Changes Business. Available online: https://www.forbes.com/sites/googlecloud/2021/06/21/how-the-cloud-telecommunications-revolution-changes-business/?sh=713312351ecb (accessed on 25 September 2022).
  6. Cloud Players Reshape Telecom’s Landscape—Industry Voices-Walker. Available online: https://www.fiercetelecom.com/telecom/cloud-players-reshape-telecom-s-landscape-industry-voices-walker (accessed on 27 September 2022).
  7. Drampalou, S.F.; Miridakis, N.I.; Leligou, H.C.; Karkazis, P.A. A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems. Signals 2023, 4, 208–234.
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  14. Alam, I.; Sharif, K.; Li, F.; Latif, Z.; Karim, M.M.; Biswas, S.; Nour, B.; Wang, Y. A survey of network virtualization techniques for Internet of Things using SDN and NFV. ACM Comput. Surv. 2020, 53, 1–40.
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Subjects: Computer Science, Information Systems
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Michael Xevgenis
,
Dimitrios G. Kogias
,
Panagiotis A. Karkazis
,
Helen C. Leligou
View Times: 143
Revisions: 2 times (View History)
Update Date: 19 Jan 2024
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