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Cyber Security in Industry 4.0 and Beyond
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Cyber Security in Industry 4.0 and Beyond

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 17904

Special Issue Editor

Dr. Radek Fujdiak

E-Mail Website
Guest Editor
Brno University of Technology, Faculty of Electrical Engineering and Communication, Brno, Czech Republic
Interests: cybersecurity; cryptography; security assessment; risk management; operational technology; smart grid; smart city; smart factory and industry 4.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Industry 4.0 brings entirely new capabilities, including autonomous systems, advanced data analytics, cloud solutions, augmented reality, systems interconnection, and other components via full industrial digitization. Information and operational technologies are fully converging, and the imaginary level between them is disappearing. As a result, emerging systems often become much more vulnerable than traditional “offline” systems. Thus, completely new challenges arise in the field of development and research in the political, academic, and private areas. This is especially true in the area of cyber security. Therefore, this Special Issue serves as a response to this new trend and welcomes new original research outputs for defending the concepts of Industry 4.0 and beyond.

Dr. Radek Fujdiak
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • Cyber security and safety in smart factories

  • Threat hunting for industrial control systems
  • Malware detection within operational technology networks
  • Lightweight cryptography for industrial Internet of Things
  • Incident detection, response, and prevention
  • Cyber risk management
  • Digital twin and advanced virtual environment,
  • Post-quantum protection in future manufacturing
  • Industry 4.0 and Industry 5.0

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Published Papers (5 papers)

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Research

17 pages, 2177 KiB  
Article
Bypassing Heaven’s Gate Technique Using Black-Box Testing
by Seon-Jin Hwang, Assem Utaliyeva, Jae-Seok Kim and Yoon-Ho Choi
Sensors 2023, 23(23), 9417; https://doi.org/10.3390/s23239417 - 26 Nov 2023
Cited by 1 | Viewed by 1921
Abstract
In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven’s Gate, enables 64-bit code to run within a 32-bit process. Heaven’s Gate exploits [...] Read more.
In recent years, the number and sophistication of malware attacks on computer systems have increased significantly. One technique employed by malware authors to evade detection and analysis, known as Heaven’s Gate, enables 64-bit code to run within a 32-bit process. Heaven’s Gate exploits a feature in the operating system that allows the transition from a 32-bit mode to a 64-bit mode during execution, enabling the malware to evade detection by security software designed to monitor only 32-bit processes. Heaven’s Gate poses significant challenges for existing security tools, including dynamic binary instrumentation (DBI) tools, widely used for program analysis, unpacking, and de-virtualization. In this paper, we provide a comprehensive analysis of the Heaven’s Gate technique. We also propose a novel approach to bypass the Heaven’s Gate technique using black-box testing. Our experimental results show that the proposed approach effectively bypasses and prevents the Heaven’s Gate technique and strengthens the capabilities of DBI tools in combating advanced malware threats. Full article
(This article belongs to the Special Issue Cyber Security in Industry 4.0 and Beyond)
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21 pages, 350 KiB  
Article
Smart Metering Cybersecurity—Requirements, Methodology, and Testing
by David Kohout, Tomas Lieskovan and Petr Mlynek
Sensors 2023, 23(8), 4043; https://doi.org/10.3390/s23084043 - 17 Apr 2023
Cited by 5 | Viewed by 4302
Abstract
This paper addresses the current challenges in cybersecurity of smart metering infrastructure, specifically in relation to the Czech Decree 359/2020 and the DLMS security suite (device language message specification). The authors present a novel testing methodology for verifying cybersecurity requirements, motivated by the [...] Read more.
This paper addresses the current challenges in cybersecurity of smart metering infrastructure, specifically in relation to the Czech Decree 359/2020 and the DLMS security suite (device language message specification). The authors present a novel testing methodology for verifying cybersecurity requirements, motivated by the need to comply with European directives and legal requirements of the Czech authority. The methodology encompasses testing cybersecurity parameters of smart meters and related infrastructure, as well as evaluating wireless communication technologies in the context of cybersecurity requirements. The article contributes by summarizing the cybersecurity requirements, creating a testing methodology, and evaluating a real smart meter, using the proposed approach. The authors conclude by presenting a methodology that can be replicated and tools that can be used to test smart meters and the related infrastructure. This paper aims to propose a more effective solution and takes a significant step towards improving the cybersecurity of smart metering technologies. Full article
(This article belongs to the Special Issue Cyber Security in Industry 4.0 and Beyond)
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19 pages, 763 KiB  
Article
Hunting Network Anomalies in a Railway Axle Counter System
by Karel Kuchar, Eva Holasova, Ondrej Pospisil, Henri Ruotsalainen, Radek Fujdiak and Adrian Wagner
Sensors 2023, 23(6), 3122; https://doi.org/10.3390/s23063122 - 14 Mar 2023
Cited by 2 | Viewed by 2946
Abstract
This paper presents a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. In contrast to the state-of-the-art works, our experimental results are validated with testbed-based real-world axle counting components. Furthermore, we aimed to detect [...] Read more.
This paper presents a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. In contrast to the state-of-the-art works, our experimental results are validated with testbed-based real-world axle counting components. Furthermore, we aimed to detect targeted attacks on axle counting systems, which have higher impacts than conventional network attacks. We present a comprehensive investigation of machine learning-based intrusion detection methods to reveal cyber attacks in railway axle counting networks. According to our findings, the proposed machine learning-based models were able to categorize six different network states (normal and under attack). The overall accuracy of the initial models was ca. 70–100% for the test data set in laboratory conditions. In operational conditions, the accuracy decreased to under 50%. To increase the accuracy, we introduce a novel input data-preprocessing method with the denoted gamma parameter. This increased the accuracy of the deep neural network model to 69.52% for six labels, 85.11% for five labels, and 92.02% for two labels. The gamma parameter also removed the dependence on the time series, enabled relevant classification of data in the real network, and increased the accuracy of the model in real operations. This parameter is influenced by simulated attacks and, thus, allows the classification of traffic into specified classes. Full article
(This article belongs to the Special Issue Cyber Security in Industry 4.0 and Beyond)
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18 pages, 1072 KiB  
Article
FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries
by Priyanka Verma, John G. Breslin and Donna O’Shea
Sensors 2022, 22(22), 8974; https://doi.org/10.3390/s22228974 - 19 Nov 2022
Cited by 13 | Viewed by 2701
Abstract
The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices [...] Read more.
The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches. Full article
(This article belongs to the Special Issue Cyber Security in Industry 4.0 and Beyond)
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20 pages, 3945 KiB  
Article
Enhanced Modbus/TCP Security Protocol: Authentication and Authorization Functions Supported
by Tiago Martins and Sergio Vidal Garcia Oliveira
Sensors 2022, 22(20), 8024; https://doi.org/10.3390/s22208024 - 20 Oct 2022
Cited by 12 | Viewed by 4719
Abstract
The Zero Trust concept is being adopted in information technology (IT) deployments, while human users remain to be the main risk for operational technology (OT) deployments. This article proposes to enhance the new Modbus/TCP Security protocol with authentication and authorization functions that guarantee [...] Read more.
The Zero Trust concept is being adopted in information technology (IT) deployments, while human users remain to be the main risk for operational technology (OT) deployments. This article proposes to enhance the new Modbus/TCP Security protocol with authentication and authorization functions that guarantee security against intentional unauthorized access. It aims to comply with the principle of never trusting the person who is accessing the network before carrying out a security check. Two functions are tested and used in order to build an access control method that is based on a username and a password for human users with knowledge of industrial automation control systems (IACS), using simple means, low motivation, and few resources. A man-in-the-middle (MITM) component was added in order to intermediate the client and the server communication and to validate these functions. The proposed scenario was implemented using the Node-RED programming platform. The tests implementing the functions and the access control method through the Node-RED software have proven their potential and their applicability. Full article
(This article belongs to the Special Issue Cyber Security in Industry 4.0 and Beyond)
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