A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security

Ersin Enes Eryılmaz; Sedat Akleylek; Yankı Ertek; Erdal Kılıç

doi:10.51354/mjen.1197753

EN

A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security

Abstract

IIoT “Industrial Internet of Things” refers to a subset of Internet of Things technology designed for industrial processes and industrial environments. IIoT aims to make manufacturing facilities, energy systems, transportation networks, and other industrial systems smarter, more efficient and connected. IIoT aims to reduce costs, increase productivity, and support more sustainable operations by making industrial processes more efficient. In this context, the use of IIoT is increasing in production, energy, healthcare, transportation, and other sectors. IoT has become one of the fastest-growing and expanding areas in the history of information technology. Billions of devices communicate with the Internet of Things with almost no human intervention. IIoT consists of sophisticated analysis and processing structures that handle data generated by internet-connected machines. IIoT devices vary from sensors to complex industrial robots. Security measures such as patch management, access control, network monitoring, authentication, service isolation, encryption, unauthorized entry detection, and application security are implemented for IIoT networks and devices. However, these methods inherently contain security vulnerabilities. As deep learning (DL) and machine learning (ML) models have significantly advanced in recent years, they have also begun to be employed in advanced security methods for IoT systems. The primary objective of this systematic survey is to address research questions by discussing the advantages and disadvantages of DL and ML algorithms used in IoT security. The purpose and details of the models, dataset characteristics, performance measures, and approaches they are compared to are covered. In the final section, the shortcomings of the reviewed manuscripts are identified, and open issues in the literature are discussed.

Keywords

industrial internet of things, IIoT security, deep learning, machine learning, Industry 4.0.

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Review

Authors

Ersin Enes Eryılmaz ^*
0000-0003-1163-970X
Türkiye

Sedat Akleylek
0000-0001-7005-6489
Türkiye

Yankı Ertek
0000-0003-3998-1419
Türkiye

Erdal Kılıç
0000-0003-1585-0991
Türkiye

Publication Date

June 21, 2024

Submission Date

November 1, 2022

Acceptance Date

February 16, 2024

Published in Issue

Year 2024 Volume: 12 Number: 1

DOI

https://doi.org/10.51354/mjen.1197753

IZ

https://izlik.org/JA99DL47FD

APA

Eryılmaz, E. E., Akleylek, S., Ertek, Y., & Kılıç, E. (2024). A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MANAS Journal of Engineering, 12(1), 1-28. https://doi.org/10.51354/mjen.1197753

AMA

1.Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. 2024;12(1):1-28. doi:10.51354/mjen.1197753

Chicago

Eryılmaz, Ersin Enes, Sedat Akleylek, Yankı Ertek, and Erdal Kılıç. 2024. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering 12 (1): 1-28. https://doi.org/10.51354/mjen.1197753.

EndNote

Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E (June 1, 2024) A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MANAS Journal of Engineering 12 1 1–28.

IEEE

[1]E. E. Eryılmaz, S. Akleylek, Y. Ertek, and E. Kılıç, “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”, MJEN, vol. 12, no. 1, pp. 1–28, June 2024, doi: 10.51354/mjen.1197753.

ISNAD

Eryılmaz, Ersin Enes - Akleylek, Sedat - Ertek, Yankı - Kılıç, Erdal. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering 12/1 (June 1, 2024): 1-28. https://doi.org/10.51354/mjen.1197753.

JAMA

1.Eryılmaz EE, Akleylek S, Ertek Y, Kılıç E. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. 2024;12:1–28.

MLA

Eryılmaz, Ersin Enes, et al. “A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security”. MANAS Journal of Engineering, vol. 12, no. 1, June 2024, pp. 1-28, doi:10.51354/mjen.1197753.

Vancouver

1.Ersin Enes Eryılmaz, Sedat Akleylek, Yankı Ertek, Erdal Kılıç. A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security. MJEN. 2024 Jun. 1;12(1):1-28. doi:10.51354/mjen.1197753

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