Securing Manufacturing Intelligence for the Industrial Internet of Things

Graham Hill; Hussain Al-Aqrabi; Philip Lane; Hamza Aagela

doi:10.1007/978-981-32-9343-4_21

Securing Manufacturing Intelligence for the Industrial Internet of Things

Graham Hill, Hussain Al-Aqrabi, Philip Lane, Hamza Aagela

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

Widespread interest in the emerging area of predictive analytics is driving the manufacturing industry to explore new approaches to the collection and management of data through Industrial Internet of Things (IIoT) devices. Analytics processing for Business Intelligence (BI) is an intensive task, presenting both a competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.

Original language	English
Title of host publication	Fourth International Congress on Information and Communication Technology
Subtitle of host publication	ICICT 2019, London, Volume 2
Editors	Xin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
Publisher	Springer Singapore
Pages	267-282
Number of pages	16
Volume	2
Edition	1
ISBN (Electronic)	9789813293434
ISBN (Print)	9789813293427, 981329342X
DOIs	https://doi.org/10.1007/978-981-32-9343-4_21
Publication status	Published - 3 Jan 2020
Event	4th International Congress on Information and Communication Technology - London, United Kingdom Duration: 27 Feb 2019 → 28 Feb 2019 Conference number: 4 http://www.icict.co.uk/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer Singapore
Volume	1027
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5357

Conference

Conference	4th International Congress on Information and Communication Technology
Abbreviated title	ICICT 2019
Country/Territory	United Kingdom
City	London
Period	27/02/19 → 28/02/19
Internet address	http://www.icict.co.uk/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-981-32-9343-4_21Licence: Unspecified

Cite this

Hill, G., Al-Aqrabi, H., Lane, P., & Aagela, H. (2020). Securing Manufacturing Intelligence for the Industrial Internet of Things. In X-S. Yang, S. Sherratt, N. Dey, & A. Joshi (Eds.), Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2 (1 ed., Vol. 2, pp. 267-282). (Advances in Intelligent Systems and Computing; Vol. 1027). Springer Singapore. https://doi.org/10.1007/978-981-32-9343-4_21

Hill, Graham ; Al-Aqrabi, Hussain ; Lane, Philip et al. / Securing Manufacturing Intelligence for the Industrial Internet of Things. Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2. editor / Xin-She Yang ; Simon Sherratt ; Nilanjan Dey ; Amit Joshi. Vol. 2 1. ed. Springer Singapore, 2020. pp. 267-282 (Advances in Intelligent Systems and Computing).

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abstract = "Widespread interest in the emerging area of predictive analytics is driving the manufacturing industry to explore new approaches to the collection and management of data through Industrial Internet of Things (IIoT) devices. Analytics processing for Business Intelligence (BI) is an intensive task, presenting both a competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.",

keywords = "Business Intelligence, Industrial Internet of Things, Security",

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Hill, G, Al-Aqrabi, H, Lane, P & Aagela, H 2020, Securing Manufacturing Intelligence for the Industrial Internet of Things. in X-S Yang, S Sherratt, N Dey & A Joshi (eds), Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2. 1 edn, vol. 2, Advances in Intelligent Systems and Computing, vol. 1027, Springer Singapore, pp. 267-282, 4th International Congress on Information and Communication Technology, London, United Kingdom, 27/02/19. https://doi.org/10.1007/978-981-32-9343-4_21

Securing Manufacturing Intelligence for the Industrial Internet of Things. / Hill, Graham; Al-Aqrabi, Hussain; Lane, Philip et al.

Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2. ed. / Xin-She Yang; Simon Sherratt; Nilanjan Dey; Amit Joshi. Vol. 2 1. ed. Springer Singapore, 2020. p. 267-282 (Advances in Intelligent Systems and Computing; Vol. 1027).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Securing Manufacturing Intelligence for the Industrial Internet of Things

AU - Hill, Graham

AU - Al-Aqrabi, Hussain

AU - Lane, Philip

AU - Aagela, Hamza

N1 - Conference code: 4

PY - 2020/1/3

Y1 - 2020/1/3

N2 - Widespread interest in the emerging area of predictive analytics is driving the manufacturing industry to explore new approaches to the collection and management of data through Industrial Internet of Things (IIoT) devices. Analytics processing for Business Intelligence (BI) is an intensive task, presenting both a competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.

AB - Widespread interest in the emerging area of predictive analytics is driving the manufacturing industry to explore new approaches to the collection and management of data through Industrial Internet of Things (IIoT) devices. Analytics processing for Business Intelligence (BI) is an intensive task, presenting both a competitive advantage as well as a security vulnerability in terms of the potential for losing Intellectual property (IP). This article explores two approaches to securing BI in the manufacturing domain. Simulation results indicate that a Unified Threat Management (UTM) model is simpler to maintain and has less potential vulnerabilities than a distributed security model. Conversely, a distributed model of security out-performs the UTM model and offers more scope for the use of existing hardware resources. In conclusion, a hybrid security model is proposed where security controls are segregated into a multi-cloud architecture.

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SN - 9789813293427

SN - 981329342X

VL - 2

T3 - Advances in Intelligent Systems and Computing

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BT - Fourth International Congress on Information and Communication Technology

A2 - Yang, Xin-She

A2 - Sherratt, Simon

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PB - Springer Singapore

T2 - 4th International Congress on Information and Communication Technology

Y2 - 27 February 2019 through 28 February 2019

ER -

Hill G, Al-Aqrabi H, Lane P, Aagela H. Securing Manufacturing Intelligence for the Industrial Internet of Things. In Yang X-S, Sherratt S, Dey N, Joshi A, editors, Fourth International Congress on Information and Communication Technology: ICICT 2019, London, Volume 2. 1 ed. Vol. 2. Springer Singapore. 2020. p. 267-282. (Advances in Intelligent Systems and Computing). Epub 2020 Jan 3. doi: 10.1007/978-981-32-9343-4_21

Securing Manufacturing Intelligence for the Industrial Internet of Things

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