Special Issue "Cyber Factories – Intelligent and Secure Factories of the Future"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (31 October 2020).

Special Issue Editor

Prof. Dr. Isabel Praça
Website
Guest Editor
School of Engineering (ISEP) / Polytechnic Institute of Porto (IPP), Porto, Portugal
Interests: Cyber Security; Machine learning; Intelligent Decision Support; Intelligent and Secure Energy Systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The digitalization of production is often described as being another industrial revolution, with a tremendous impact on our lives as members of the workforce and as consumers. With the increase of distributed connected devices and the so-called industrial internet of things (IIoT), factories are facing a digital transformation that brings challenges concerning their efficiency, security and safety. The paradigm is now based on the transaction of data rather than on the transaction of goods. Any connected device, whether from the shop floor or remotely from third parties, despite the valuable data from the optimization and business point of view, needs to be considered as a risk.

Data collected from different sources and domains feeds data lakes, which—if properly exploited through artificial intelligence techniques—can effectively contribute to process optimization, including predictive manufacturing, and bring new data-centered business opportunities. At the same time, artificial intelligence techniques can also be employed to prevent, detect, and respond to cyber incidents. Moreover, a combination of cyber and physical breaches brings complex attack scenarios that can only be mitigated with a holistic view of security, with different techniques but covering all of the cyber security life-cycle.

Another challenge in the factories of the future is the way human–machine interactions are changing from a context where humans operate machines to the one where humans and robots collaborate. An environment with a straight collaboration between humans and robots will benefit from methodologies that can monitor human and machine behavior and adapt both to operate safely.

The aim of this Special Issue is to provide a forum for the dissemination of works in which academics and industrial practitioners discuss the opportunities and threats of the factories of the future, and how artificial intelligence can impact on them.

Prof. Dr. Isabel Praça
Guest Editor

Manuscript Submission Information

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Keywords

  • Adversarial machine learning
  • Artificial intelligence for optimization and security in cyber factories
  • Collaborative robotics
  • Correlation of cyber–physical threats
  • Cyber awareness in manufacturing environments
  • Cyber range
  • Digital twins
  • Energy efficiency in manufacturing 
  • Explainable artificial intelligence
  • Factories of the future use cases
  • Human–machine interaction
  • Industrial intrusion detection systems
  • Investigation systems
  • IT/OT risk assessment
  • Optimization
  • Predictive maintenance
  • Systems interoperability and log semantic

Published Papers (5 papers)

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Research

Open AccessArticle
Beyond 5G Network Architecture Study: Fractal Properties of Access Network
Appl. Sci. 2020, 10(20), 7191; https://doi.org/10.3390/app10207191 - 15 Oct 2020
Abstract
Wireless networks connect various devices through radio waves in which the network connection may have different structures. Moreover, the network structure is determined based on the placement areas of the network elements, which can be affected by the building and their locations. However, [...] Read more.
Wireless networks connect various devices through radio waves in which the network connection may have different structures. Moreover, the network structure is determined based on the placement areas of the network elements, which can be affected by the building and their locations. However, the numerical characteristic which describe the features of the real environment and allow them to be related to the properties of the model are still a challenge that has not been well addressed. To this end, in this paper, we analyze the modeling problems related to the structure of user placement in the access network. Our proposed solution is based on a description of the user environment structure in which cities in the form of buildings and constructions are considered as a typical environment. We propose a new model for addressing the wireless network structure in an efficient manner in which the features of the environment are considered, which are numerically expressed in the form of the Hurst parameter or fractal dimension. In addition, the fractal dimension, geometric fractals, and the characteristics of the user’s distribution territory and urban development are efficiently utilized. Then, we analyze the influence of the fractal properties of the environment on the structure of promising communication networks; in particular, on the structure of the Internet of Things network. Finally, simulation results proved that the proposed model is considered as a beneficial solution for modeling mobile communication and wireless access networks, including fifth-generation networks. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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Open AccessArticle
An Open Source Framework Approach to Support Condition Monitoring and Maintenance
Appl. Sci. 2020, 10(18), 6360; https://doi.org/10.3390/app10186360 - 12 Sep 2020
Abstract
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and [...] Read more.
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture–condition-based maintenance (OSA–CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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Open AccessArticle
A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future
Appl. Sci. 2020, 10(13), 4482; https://doi.org/10.3390/app10134482 - 28 Jun 2020
Cited by 1
Abstract
In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on [...] Read more.
In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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Open AccessArticle
Cyber Threat Actors for the Factory of the Future
Appl. Sci. 2020, 10(12), 4334; https://doi.org/10.3390/app10124334 - 24 Jun 2020
Abstract
The increasing degree of connectivity in factory of the future (FoF) environments, with systems that were never designed for a networked environment in terms of their technical security nature, is accompanied by a number of security risks that must be considered. This leads [...] Read more.
The increasing degree of connectivity in factory of the future (FoF) environments, with systems that were never designed for a networked environment in terms of their technical security nature, is accompanied by a number of security risks that must be considered. This leads to the necessity of relying on risk assessment-based approaches to reach a sufficiently mature cyber security management level. However, the lack of common definitions of cyber threat actors (CTA) poses challenges in untested environments such as the FoF. This paper analyses policy papers and reports from expert organizations to identify common definitions of CTAs. A significant consensus exists only on two common CTAs, while other CTAs are often either ignored or overestimated in their importance. The identified motivations of CTAs are contrasted with the specific characteristics of FoF environments to determine the most likely CTAs targeting FoF environments. Special emphasis is given to corporate competitors, as FoF environments probably provide better opportunities than ever for industrial espionage if they are not sufficiently secured. In this context, the study aims to draw attention to the research gaps in this area. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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Open AccessArticle
Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
Appl. Sci. 2020, 10(7), 2596; https://doi.org/10.3390/app10072596 - 10 Apr 2020
Abstract
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not [...] Read more.
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work. Full article
(This article belongs to the Special Issue Cyber Factories – Intelligent and Secure Factories of the Future)
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