A Multi-layer Security Model for 5G-Enabled Industrial Internet of Things

Al-Aqrabi, Hussain; Johnson, Anju P.; Hill, Richard; Lane, Phil; Liu, Lu

doi:10.1007/978-981-15-1301-5_23

Cited by 17 publications

(8 citation statements)

References 27 publications

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“…For example, in order to design and develop a new hybrid DI approach that is mixed up from various technologies such as Network Function Virtualization (NFV) [93], Mobile cloud Computing [94], Multi-Agent system [95,96], Distributed Leger technology [97], is yet to be developed. -IOTA DLT Masked Authentication Messaging comprises of three modes including public, private, and restricted that could potentially provide a way of enabling multiparty authentication scenarios [4], and access control. Moreover, location privacy [13] that focuses on how to effectively select reasonable dummy locations and avoid having the real locations.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

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Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

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Widespread adoption of smart IoT devices is accelerating research for new techniques to make IoT applications secure, scalable, energy-efficient, and capable of working in mission-critical use cases, which require an ability to function offline. In this context, the novel combination of distributed ledger technology (DLT) and distributed intelligence (DI) is seen as a practical route towards the decentralisation of IoT architectures. This paper surveys DI techniques in IoT and commences by briefly explaining the need for DI, by proposing a comprehensive taxonomy of DI in IoT. This taxonomy is then used to review existing techniques and to investigate current challenges that require careful attention and consideration. Based on the taxonomy, IoT DI techniques can be classified into five categories based on the factors that support distributed functionality and data acquisition: cloud-computing, mist-computing, distributed-ledger-technology, service-oriented-computing and hybrid. Existing techniques are compared and categorized mainly based on related challenges, and the level of intelligence supported. We evaluate more than thirty current research efforts in this area. We define many significant functionalities that should be supported by DI frameworks and solutions. Our work assists system architects and developers to select the correct low-level communication techniques in an integrated IoT-to-DLT-to-cloud system architecture. The benefits and shortcomings of different DI approaches are presented, which will inspire future work into automatic hybridization and adaptation of DI mechanisms. Finally, open research issues for distributed intelligence in IoT are discussed.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

“…By 2030, the study predicts that IoT will rise exponentially, for example, by about 125 billion connected devices to the internet [1][2][3]. As a result, this poses several challenges in terms of providing timely delivery, data volume, speed, confidentiality and scalability [4,5].…”

Section: Introductionmentioning

confidence: 99%

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

et al. 2021

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“…In NIST layers there are tenant users which could be hardware devices or virtual machines (VMs). Such a model may be implemented to each layer according to the principles of trustworthy computing [1,2].…”

Section: Nist Cloud Modelmentioning

confidence: 99%

“…It is clear that raising industrial users' knowledge that a substantial amount of the interest they create is intrinsically connected with intellectual property (IP) ownership and continuous development. There is also the persistent risk of a security breach that could compromise ownership of the IP, putting the underlying business model at higher risk [2,3]. This is particularly prevalent in the provision of IoT assisted healthcare systems, which is a pertinent example of distributed IT systems that have similarly complex needs and stakeholder requirements [4].…”

Section: Introductionmentioning

confidence: 99%

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Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

Al-Aqrabi

Johnson

Hill

et al. 2020

Sensors

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The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.

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Towards a Scalable IOTA Tangle-Based Distributed Intelligence Approach for the Internet of Things

Alsboui

Qin

Hill

et al. 2020

Advances in Intelligent Systems and Computing

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Distributed Ledger Technology (DLT) brings a set of opportunities for the Internet of Things (IoT), which leads to innovative solutions for existing components at all levels of existing architectures. IOTA Tangle has the potential to overcome current technical challenges identified for the IoT domain, such as data processing, infrastructure scalability, security, and privacy. Scaling is a serious challenge that influences the deployment of IoT applications. We propose a Scalable Distributed Intelligence Tangle-based approach (SDIT), which aims to address the scalability problem in IoT by adapting the IOTA Tangle architecture. It allows the seamless integration of new IoT devices across different applications. In addition, we describe an offloading mechanism to perform proof-of-work (PoW) computation in an energy-efficient way. A set of experiments has been conducted to prove the feasibility of the Tangle in achieving better scalability, while maintaining energy efficiency. The results indicate that our proposed solution provides highly-scalable and energy efficient transaction processing for IoT DLT applications, when compared with an existing DAG-based distributed ledger approach.

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A Multi-layer Security Model for 5G-Enabled Industrial Internet of Things

Cited by 17 publications

References 27 publications

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Distributed Intelligence in the Internet of Things: Challenges and Opportunities

Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

Towards a Scalable IOTA Tangle-Based Distributed Intelligence Approach for the Internet of Things

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