Artificial Intelligence and Machine Learning in Cybersecurity Frontiers: Insights from Industry 4.0 and Innovations for Industry 5.0

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 August 2024 | Viewed by 3043

Special Issue Editors

Software Research Institute, N37 A3W4 Athlone, Ireland
Interests: network security; machine learning; network traffic control; multimedia communication
School of Computer Science and Mathematics, Keele University, Staffordshire ST5 5GB, UK
Interests: machine learning; computer vision; image processing; visual data; privacy; security; object classification; activity recognition; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Software Research Institute, Technological University of The Shannon, Midlands Midwest, N37 HD68 Athlone, Ireland
Interests: network security; machine learning; robotic control; network management; edge computing; IoT
Physical, Mathematical and Engineering Sciences, University of Chester, Parkgate Road, Chester CH1 4BJ, UK
Interests: neural networks; deep learning; IoT; smart cities; resource-efficient machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we embrace the digital revolution brought by Industry 4.0, cybersecurity has emerged as a crucial area of concern. In this dynamic landscape, machine learning (ML) and deep learning (DL) technologies have shown tremendous promise in addressing the complexity and scale of cybersecurity issues yet have also introduced novel vulnerabilities. To navigate this double-edged sword, we must foster a deeper understanding of the intersection of ML, DL, and cybersecurity.

In this Special Issue, titled "Machine Learning Security in Industry 4.0: Opportunities, Challenges, and Innovations," we invite scholars and professionals from around the globe to share their cutting-edge research, innovative strategies, and insightful experiences. We aim to create a knowledge hub that sparks exciting discussions, advances scientific understanding, and catalyzes transformative solutions for securing our digital future.

The Special Issue will spotlight a broad spectrum of research areas, including:

  • Innovative Intrusion Detection Models: Can we leverage the power of ML to design more efficient and effective intrusion detection systems? We seek pioneering works that break the mold, exploring novel ML algorithms and architectures for anomaly detection and cybersecurity breach prevention.
  • Revolutionizing Risk Assessment: How can ML help predict and quantify the potential impact of cyber threats? We invite visionary contributions that redefine risk assessment paradigms, harnessing ML to identify, analyze, and mitigate cybersecurity risks in industrial systems.
  • Automated Incident Response and Playbook Design: How might ML transform our approach to incident response? This is an open call for revolutionary ideas that integrate ML into incident response strategies and playbook design, enabling rapid, intelligent responses to cyber incidents.
  • Next-Level Threat Intelligence Sharing: Can ML facilitate real-time, comprehensive threat intelligence sharing? We welcome groundbreaking research on ML-driven platforms that foster seamless information exchange, building robust, collaborative defenses against emerging threats.
  • Securing ML Models Against Cyber Attacks: How can we safeguard our ML models from adversarial manipulation? We are eager to showcase ingenious research on identifying and thwarting potential attack vectors, including adversarial and backdoor attacks on neural networks. Adversarial: The Role of Large Language Models in Cybersecurity: What unique possibilities and challenges do advanced models such as GPT series bring to the cybersecurity landscape? We invite forward-thinking explorations on employing large language models for threat detection, phishing detection, and other cybersecurity applications.

We welcome submissions in various formats, from original research and review articles to case studies and more. Each contribution will be an integral part of our collective endeavor to advance this vital field, inspiring fellow researchers, guiding policy-makers, and ultimately safeguarding our Industry 4.0 systems.

Join us in this exciting quest to illuminate the frontiers of Machine Learning Security in Industry 4.0 and shape the future of cybersecurity. Your insights could be the catalyst for the next big breakthrough in this fast-evolving field.

Dr. Yuhang Ye
Dr. Nadia Kanwal
Dr. Brian Lee
Dr. Mohammad Samar Ansari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • backdoor
  • adversarial learning
  • cybersecurity
  • Industry 4.0 industrial control system
  • ICS
  • SCADA
  • risk assessment
  • digital twin
  • trigger
  • data poisoning
  • model poisoning
  • overfitting
  • model robustness

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

28 pages, 4406 KiB  
Article
Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems
by Thierno Gueye, Asif Iqbal, Yanen Wang, Ray Tahir Mushtaq and Mohd Iskandar Petra
Electronics 2024, 13(5), 837; https://doi.org/10.3390/electronics13050837 - 21 Feb 2024
Viewed by 621
Abstract
This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness [...] Read more.
This research addresses the dearth of real-world data required for effective neural network model building, delving into the crucial field of industrial control and automation system (ICS) cybersecurity. Cyberattacks against ICS are first identified and then generated in an effort to raise awareness of vulnerabilities and improve security. This research aims to fill a need in the existing literature by examining the effectiveness of a novel approach to ICS cybersecurity that draws on data from real industrial settings. Real-world data from a variety of commercial sectors is used in this study to produce a complete dataset. These sectors include power systems, freshwater tanks, and gas pipelines, which together provide a wide range of commercial scenarios where anomaly detection and attack classification approaches are critical. The generated data are shown to considerably improve the models’ precision. An amazing 71% accuracy rate is achieved in power system models, and incorporating generated data reliably increases network speed. Using generated data, the machine learning system achieves an impressive 99% accuracy in a number of trials. In addition, the system shows about 90% accuracy in most studies when applied to the setting of gas pipelines. In conclusion, this article stresses the need to improve cybersecurity in vital industrial sectors by addressing the dearth of real-world ICS data. To better understand and defend against cyberattacks on industrial machinery and automation systems, it demonstrates how generative data can improve the precision and dependability of neural network models. Full article
Show Figures

Figure 1

Review

Jump to: Research

15 pages, 443 KiB  
Review
AI in IIoT Management of Cybersecurity for Industry 4.0 and Industry 5.0 Purposes
by Grzegorz Czeczot, Izabela Rojek, Dariusz Mikołajewski and Belco Sangho
Electronics 2023, 12(18), 3800; https://doi.org/10.3390/electronics12183800 - 08 Sep 2023
Cited by 2 | Viewed by 1831
Abstract
If we look at the chronology of transitions between successive stages of industrialization, it is impossible not to notice a significant acceleration. There were 100 years between the industrial revolutions from 2.0 to 3.0, and only half a century passed from the conventional [...] Read more.
If we look at the chronology of transitions between successive stages of industrialization, it is impossible not to notice a significant acceleration. There were 100 years between the industrial revolutions from 2.0 to 3.0, and only half a century passed from the conventional 3.0 to 4.0. Assuming that progress will inevitably continue to accelerate, and given that 2011 is the set date for the start of the fourth industrial revolution, we can expect Industry 5.0 by 2035. In recent years, Industrial Internet of Things (IIoT) applications proliferated, which include multiple network elements connected by wired and wireless communication technologies, as well as sensors and actuators placed in strategic locations. The significant pace of development of the industry of advantages in predicting threats to infrastructure will be related to the speed of analyzing the huge amount of data on threats collected not locally, but globally. This article sheds light on the potential role of artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), to significantly impact IIoT cyber threat prediction in Industry 5.0. Full article
Show Figures

Figure 1

Back to TopTop