Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Data Protection and Privacy in Industry 4.0 Era
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Data Protection and Privacy in Industry 4.0 Era

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

Deadline for manuscript submissions: 20 November 2024 | Viewed by 4597

Special Issue Editors

Prof. Dr. Chrysostomos Stylios

E-Mail Website
Guest Editor
Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: security; privacy; VR; AI; data structures; machine learning; industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Kyriakos Stefanidis

E-Mail Website
Guest Editor
Industrial Systems Institute, 26504 Athena, Greece
Interests: cybersecurity; incident response; data security; intrusion detection and malware analysis social media account
Special Issues, Collections and Topics in MDPI journals
Dr. Vasiliki Liagkou

E-Mail Website
Guest Editor
Department of Informatics & Telecommunications, University of Ioannina, 45110 Ioannina, Greece
Interests: system cryptanalysis; system security; trust management; pseudorandom generators; algorithm engineering; number theory; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 includes many technological aspects that have led to an integrated digital manufacturing environment. The thoroughly interconnected ecosystem of Industry 4.0 has to meet many security challenges and threats for each component. Preserving security plays a crucial role in Industry 4.0, and it is vital for its existence; the key issue is how to ensure the confidentiality, integrity, and availability of the information shared among the Industry 4.0 components.

In addition to this, the significant and rapid inclusion of the Internet of Things (IoT) in our daily lives, together with the rapidly increasing number of cyber security incidents, further stress the need to strengthen cyber resilience and preserving users’ privacy for protecting the information of individuals from exposure in the IoT environment. The large attack surface in terms of connected devices and the complex processes involved in the IoT ecosystem can lead to more sophisticated physical attacks on IoT systems.

With such a wide attack surface, these innovative and emerging infrastructures and applications based on IoT can effectively serve their purpose only if privacy and security challenges are addressed.

This Special Issue aims to solicit high-quality research articles addressing key challenges and state-of-the-art solutions for security and privacy issues related to Industry 4.0 technologies and applications.

Prof. Dr. Chrysostomos Stylios
Dr. Kyriakos Stefanidis
Dr. Vasiliki Liagkou
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. Sensors 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 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

  • cybersecurity and privacy in industrial environments
  • security in cyber–physical environments
  • cryptography in I4.0
  • security and privacy in industrial control systems
  • IoT security and privacy
  • IoT system and network security
  • privacy protection and privacy-by-design
  • blockchains and smart contracts for IoT
  • trust issues in intelligent IoT devices
  • IoT threat detection and risk management
  • incident response and vulnerability management in IoT infrastructures
  • IoT privacy protection
  • secure data management and trading in industrial environments
  • privacy-enhancing technologies for ΙοΤ devices
  • IoT Identity management
  • artificial intelligence (AI)-based security
  • machine learning and data protection for I4.0
  • standardization activities for I4.0 security
  • quantum and post-quantum I4.0 cryptography
  • IoT side-channel attacks

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

22 pages, 522 KiB  
Article
Revolutionizing SIEM Security: An Innovative Correlation Engine Design for Multi-Layered Attack Detection
by Muhammad Sheeraz, Muhammad Hanif Durad, Muhammad Arsalan Paracha, Syed Muhammad Mohsin, Sadia Nishat Kazmi and Carsten Maple
Sensors 2024, 24(15), 4901; https://doi.org/10.3390/s24154901 - 28 Jul 2024
Viewed by 1223
Abstract
Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are [...] Read more.
Advances in connectivity, communication, computation, and algorithms are driving a revolution that will bring economic and social benefits through smart technologies of the Industry 4.0 era. At the same time, attackers are targeting this expanded cyberspace to exploit it. Therefore, many cyberattacks are reported each year at an increasing rate. Traditional security devices such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), anti-viruses, and the like, often cannot detect sophisticated cyberattacks. The security information and event management (SIEM) system has proven to be a very effective security tool for detecting and mitigating such cyberattacks. A SIEM system provides a holistic view of the security status of a corporate network by analyzing log data from various network devices. The correlation engine is the most important module of the SIEM system. In this study, we propose the optimized correlator (OC), a novel correlation engine that replaces the traditional regex matching sub-module with a novel high-performance multiple regex matching library called “Hyperscan” for parallel log data scanning to improve the performance of the SIEM system. Log files of 102 MB, 256 MB, 512 MB, and 1024 MB, generated from log data received from various devices in the network, are input into the OC and simple event correlator (SEC) for applying correlation rules. The results indicate that OC is 21 times faster than SEC in real-time response and 2.5 times more efficient in execution time. Furthermore, OC can detect multi-layered attacks successfully. Full article
(This article belongs to the Special Issue Data Protection and Privacy in Industry 4.0 Era)
►▼ Show Figures

Figure 1

24 pages, 1498 KiB  
Article
BEC Defender: QR Code-Based Methodology for Prevention of Business Email Compromise (BEC) Attacks
by Anastasios Papathanasiou, George Liontos, Georgios Paparis, Vasiliki Liagkou and Euripides Glavas
Sensors 2024, 24(5), 1676; https://doi.org/10.3390/s24051676 - 5 Mar 2024
Viewed by 1336
Abstract
In an era of ever-evolving and increasingly sophisticated cyber threats, protecting sensitive information from cyberattacks such as business email compromise (BEC) attacks has become a top priority for individuals and enterprises. Existing methods used to counteract the risks linked to BEC attacks frequently [...] Read more.
In an era of ever-evolving and increasingly sophisticated cyber threats, protecting sensitive information from cyberattacks such as business email compromise (BEC) attacks has become a top priority for individuals and enterprises. Existing methods used to counteract the risks linked to BEC attacks frequently prove ineffective because of the continuous development and evolution of these malicious schemes. This research introduces a novel methodology for safeguarding against BEC attacks called the BEC Defender. The methodology implemented in this paper augments the authentication mechanisms within business emails by employing a multi-layered validation process, which includes a MAC address as an identity token, QR code generation, and the integration of timestamps as unique identifiers. The BEC-Defender algorithm was implemented and evaluated in a laboratory environment, exhibiting promising results against BEC attacks by adding an extra layer of authentication. Full article
(This article belongs to the Special Issue Data Protection and Privacy in Industry 4.0 Era)
►▼ Show Figures

Figure 1

Review

Jump to: Research

38 pages, 2585 KiB  
Review
A Comprehensive Survey on Deep Learning-Based LoRa Radio Frequency Fingerprinting Identification
by Aqeel Ahmed, Bruno Quoitin, Alexander Gros and Veronique Moeyaert
Sensors 2024, 24(13), 4411; https://doi.org/10.3390/s24134411 - 8 Jul 2024
Viewed by 1191
Abstract
LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns [...] Read more.
LoRa enables long-range communication for Internet of Things (IoT) devices, especially those with limited resources and low power requirements. Consequently, LoRa has emerged as a popular choice for numerous IoT applications. However, the security of LoRa devices is one of the major concerns that requires attention. Existing device identification mechanisms use cryptography which has two major issues: (1) cryptography is hard on the device resources and (2) physical attacks might prevent them from being effective. Deep learning-based radio frequency fingerprinting identification (RFFI) is emerging as a key candidate for device identification using hardware-intrinsic features. In this paper, we present a comprehensive survey of the state of the art in the area of deep learning-based radio frequency fingerprinting identification for LoRa devices. We discuss various categories of radio frequency fingerprinting techniques along with hardware imperfections that can be exploited to identify an emitter. Furthermore, we describe different deep learning algorithms implemented for the task of LoRa device classification and summarize the main approaches and results. We discuss several representations of the LoRa signal used as input to deep learning models. Additionally, we provide a thorough review of all the LoRa RF signal datasets used in the literature and summarize details about the hardware used, the type of signals collected, the features provided, availability, and size. Finally, we conclude this paper by discussing the existing challenges in deep learning-based LoRa device identification and also envisage future research directions and opportunities. Full article
(This article belongs to the Special Issue Data Protection and Privacy in Industry 4.0 Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop