Securing Manufacturing Business Intelligence for the Industrial Internet of Things

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationFourth International Congress on Information and Communication Technology
Subtitle of host publicationICICT 2019, London, Volume 2
EditorsXin-She Yang, Simon Sherratt, Nilanjan Dey, Amit Joshi
PublisherSpringer Singapore
Number of pages16
Volume2
Edition1st
ISBN (Electronic)9789813293434
ISBN (Print)9789813293427, 981329342X
Publication statusAccepted/In press - 22 Jan 2019
EventFourth International Congress on Information and Communication Technology - London, United Kingdom
Duration: 25 Feb 201926 Feb 2019
http://www.icict.co.uk/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer Singapore
Volume1027
ISSN (Print)2194-5357
ISSN (Electronic)2194-5357

Conference

ConferenceFourth International Congress on Information and Communication Technology
Abbreviated titleICICT 2019
CountryUnited Kingdom
CityLondon
Period25/02/1926/02/19
Internet address

Fingerprint

Competitive intelligence
Intellectual property
Internet of things
Hardware
Processing
Industry

Cite this

Hill, G., Al-Aqrabi, H., Lane, P., & Aagela, H. (Accepted/In press). Securing Manufacturing Business 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 (1st ed., Vol. 2). [174] (Advances in Intelligent Systems and Computing; Vol. 1027). Springer Singapore.
Hill, Graham ; Al-Aqrabi, Hussain ; Lane, Philip ; Aagela, Hamza. / Securing Manufacturing Business 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 1st. ed. Springer Singapore, 2019. (Advances in Intelligent Systems and Computing).
@inproceedings{b56df3685b3640afbe55057e13f3618c,
title = "Securing Manufacturing Business Intelligence for the Industrial Internet of Things",
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 = "IoT",
author = "Graham Hill and Hussain Al-Aqrabi and Philip Lane and Hamza Aagela",
year = "2019",
month = "1",
day = "22",
language = "English",
isbn = "9789813293427",
volume = "2",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Singapore",
editor = "Xin-She Yang and Simon Sherratt and Nilanjan Dey and Amit Joshi",
booktitle = "Fourth International Congress on Information and Communication Technology",
address = "Singapore",
edition = "1st",

}

Hill, G, Al-Aqrabi, H, Lane, P & Aagela, H 2019, Securing Manufacturing Business 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. 1st edn, vol. 2, 174, Advances in Intelligent Systems and Computing, vol. 1027, Springer Singapore, Fourth International Congress on Information and Communication Technology, London, United Kingdom, 25/02/19.

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

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 1st. ed. Springer Singapore, 2019. 174 (Advances in Intelligent Systems and Computing; Vol. 1027).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Securing Manufacturing Business Intelligence for the Industrial Internet of Things

AU - Hill, Graham

AU - Al-Aqrabi, Hussain

AU - Lane, Philip

AU - Aagela, Hamza

PY - 2019/1/22

Y1 - 2019/1/22

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.

KW - IoT

UR - https://www.springer.com/gp/book/9789813293427

M3 - Conference contribution

SN - 9789813293427

SN - 981329342X

VL - 2

T3 - Advances in Intelligent Systems and Computing

BT - Fourth International Congress on Information and Communication Technology

A2 - Yang, Xin-She

A2 - Sherratt, Simon

A2 - Dey, Nilanjan

A2 - Joshi, Amit

PB - Springer Singapore

ER -

Hill G, Al-Aqrabi H, Lane P, Aagela H. Securing Manufacturing Business 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. 1st ed. Vol. 2. Springer Singapore. 2019. 174. (Advances in Intelligent Systems and Computing).