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Machines
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Cybersecurity for Manufacturing Factories in Industry 4.0
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Cybersecurity for Manufacturing Factories in Industry 4.0

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 5870

Special Issue Editor

Prof. Dr. Richard Hill

E-Mail Website
Guest Editor
Department of Computer Science, Centre for Industrial Analytics (CIndA), School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK
Interests: intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Industry 4.0 movement is promoting increased connectivity between smart devices. Inexpensive computational devices such as single board computers are enabling the manufacturing industry to realise considerable increases in efficiencies and performance, as previously disconnected processes are now being interlinked into digital supply chains.

Cyberphysical systems exploit the interconnectedness of Industry 4.0 and use a variety of data sources to inform the control and actuation of physical entities, such as those in manufacturing systems.

Such tight integration raises concerns for cybersecurity and trust, whether it be from nefarious agents who wish to conduct industrial espionage or from system vulnerabilities that may interrupt the smooth operation of supply chains.

This Special Issue explores the design, implementation, and evaluation of cybersecurity technologies and approaches as they are utilized to support the realisation of Industry 4.0.

A fundamental component of Industry 4.0 is the ability to sense physical phenomena and provide data as an input. This Special Issue directly addresses the need to work with sensors and sensor data, especially with regard to how we can ensure that data are held and transported securely.

Prof. Dr. Richard Hill
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. Machines is an international peer-reviewed open access monthly 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

  • industry 4.0
  • cybersecurity
  • distributed ledger technology
  • blockchain
  • intrusion detection
  • wireless network security
  • secure cyberphysical systems
  • trust
  • model checking and verification of secure systems
  • wireless sensor networks

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Published Papers (2 papers)

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Research

19 pages, 988 KiB  
Article
Securing Blockchain-Based Supply Chain Workflow against Internal and External Attacks
by Sana Al-Farsi, Halima Bensmail and Spiridon Bakiras
Machines 2022, 10(6), 431; https://doi.org/10.3390/machines10060431 - 31 May 2022
Cited by 8 | Viewed by 3050
Abstract
Blockchain is a revolutionary technology that is being used in many applications, including supply chain management. The primary goal of using a blockchain for supply chain management is to reduce the overall production cost while providing comprehensive security to the system. However, current [...] Read more.
Blockchain is a revolutionary technology that is being used in many applications, including supply chain management. The primary goal of using a blockchain for supply chain management is to reduce the overall production cost while providing comprehensive security to the system. However, current blockchain-based supply-chain workflow(s) (BSW) are still susceptible to various cyber threats due to evolving business processes of different stakeholders involved in the process. In fact, current BSW protects the supply chain process based on the rules that have been implemented in the corresponding smart contracts. However, in practice, the requirements for the process keep evolving due to several organizational policies and directives of the involved stakeholders; therefore, current blockchain-based solutions fail to protect the supply chain process against attacks that exploit the process-related information that is not protected by smart contracts. Therefore, the goal of this work was to develop a methodology that enhances the protection of BSW against various internal (e.g., Stuxnet) and external (e.g., local data breach of a stakeholder) cyber threats through monitoring the stakeholder business process. Our methodology complements the blockchain-based solution because it protects the stakeholder’s local process against the attacks that exploit the process information that is not protected in the smart contracts. We implemented a prototype and demonstrated its application to a typical supply chain workflow example application by successfully detecting internal and external attacks to the application. Full article
(This article belongs to the Special Issue Cybersecurity for Manufacturing Factories in Industry 4.0)
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17 pages, 5321 KiB  
Article
A Fast Method for Protecting Users’ Privacy in Image Hash Retrieval System
by Liang Huang, Yu Zhan, Chao Hu and Ronghua Shi
Machines 2022, 10(4), 278; https://doi.org/10.3390/machines10040278 - 14 Apr 2022
Viewed by 1947
Abstract
Effective search engines based on deep neural networks (DNNs) can be used to search for many images, as is the case with the Google Images search engine. However, the illegal use of search engines can lead to serious compromises of privacy. Affected by [...] Read more.
Effective search engines based on deep neural networks (DNNs) can be used to search for many images, as is the case with the Google Images search engine. However, the illegal use of search engines can lead to serious compromises of privacy. Affected by various factors such as economic interests and service providers, hackers and other malicious parties can steal and tamper with the image data uploaded by users, causing privacy leakage issues in image hash retrieval. Previous work has exploited the adversarial attack to protect the user’s privacy with an approximation strategy in the white-box setting, although this method leads to slow convergence. In this study, we utilized the penalty norm, which sets a strict constraint to quantify the feature of a query image into binary code via the non-convex optimization process. Moreover, we exploited the forward–backward strategy to solve the vanishing gradient caused by the quantization function. We evaluated our method on two widely used datasets and show an attractive performance with high convergence speed. Moreover, compared with other image privacy protection methods, our method shows the best performance in terms of privacy protection and image quality. Full article
(This article belongs to the Special Issue Cybersecurity for Manufacturing Factories in Industry 4.0)
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