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Industrial Cybersecurity
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Industrial Cybersecurity

A special issue of Applied System Innovation (ISSN 2571-5577).

Deadline for manuscript submissions: 30 June 2025 | Viewed by 6865

Special Issue Editor

Prof. Dr. Felix J. Garcia Clemente

E-Mail Website
Guest Editor
Faculty of Computer Science, University of Murcia, 30100 Murcia, Spain
Interests: cybersecurity; network management; Industry 4.0; learning analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to this Special Issue on recent advances and future challenges in industrial cybersecurity.

With the rapid advancement of technology and the increasing use of connected devices in industrial settings, cybersecurity has become a critical concern in the industry. The integration of Industrial Internet of Things (IIoT) devices, big data analytics, artificial intelligence, and cloud computing has provided a wealth of opportunities to improve productivity and efficiency. However, this integration also exposes industrial systems to a wide range of cyber threats that can cause serious issues, including data breaches, system failures, and other cyber attacks. As a result, it is crucial to prioritize the development and implementation of robust cybersecurity measures to protect critical systems, data, and assets from cyber threats. Due to the proliferation and sophistication of cyber threats targeting industrial control systems, industrial cybersecurity has become an agenda item for researchers, practitioners, and policymakers. New solutions and proposals that overcome the limitations of conventional security approaches are essential to handle industrial cybersecurity effectively.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of industrial cybersecurity. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. Felix J. Garcia Clemente
Guest Editor

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. Applied System Innovation 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 1400 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

  • Industrial Internet of Things (IIoT) security
  • industrial control systems (ICS) security
  • cyber–physical systems (CPS) security
  • critical infrastructure protection
  • industrial security management

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Published Papers (2 papers)

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Research

21 pages, 4312 KiB  
Article
Secure Aviation Control through a Streamlined ADS-B Perception System
by Qasem Abu Al-Haija and Ahmed Al-Tamimi
Appl. Syst. Innov. 2024, 7(2), 27; https://doi.org/10.3390/asi7020027 - 26 Mar 2024
Viewed by 1857
Abstract
Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join [...] Read more.
Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset. Full article
(This article belongs to the Special Issue Industrial Cybersecurity)
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16 pages, 5007 KiB  
Article
Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems
by Woo-Hyun Choi and Jongwon Kim
Appl. Syst. Innov. 2024, 7(2), 18; https://doi.org/10.3390/asi7020018 - 21 Feb 2024
Cited by 5 | Viewed by 4173
Abstract
Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in [...] Read more.
Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations. Full article
(This article belongs to the Special Issue Industrial Cybersecurity)
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