Securing the Future: Challenges and Prospects in Emerging Cyber‒Physical Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 2576

Special Issue Editors


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Guest Editor
Faculty of Automation and Computer Science, Department of Automation, Technical University of Cluj-Napoca, Memorandumului 28, 400014 Cluj-Napoca, Romania
Interests: cyber-physical systems; multiagent systems; computer aided design
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Guest Editor
System Laboratory, University Politehnica of Bucharest, 060042 București, Romania
Interests: cyber security; Internet of Things; networking and human-computer interaction

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Guest Editor
Faculty of Automation and Computer Science, Department of Automation, Technical University of Cluj-Napoca, Memorandumului 28, 400014 Cluj-Napoca, Romania
Interests: semantic interoperability; information management in the age of the Internet; cloud-fog-edge; dependable systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to shed light on the critical issue of security in the context of cyber‒physical systems (CPS) and explore the challenges and development prospects in this rapidly evolving field. As cyber‒physical systems become increasingly pervasive in various domains such as smart cities, autonomous vehicles, industrial automation, healthcare, and beyond, ensuring their security becomes paramount. The integration of physical components with computational elements and network connectivity introduces unique vulnerabilities that need to be addressed to safeguard these systems from cyber threats, data breaches, privacy violations, and potential physical harm.

By exploring the security aspects of emerging cyber‒physical systems, we aim to provide a platform for researchers to present novel solutions, methodologies, and insights that contribute to advancing the field of CPS security. The Special Issue seeks to foster collaboration and knowledge exchange among experts from various domains, including but not limited to electrical engineering, computer science, information security, communication networks, and system integration.

We invite researchers to submit original research articles and reviews covering a wide range of topics related to the security challenges and development prospects in emerging cyber‒physical systems. The suggested themes include, but are not limited to:

  • Threat modeling and risk assessment in cyber‒physical systems;
  • Security protocols and mechanisms for secure communication and data transfer;
  • Intrusion detection and prevention systems for cyber‒physical environments;
  • Privacy preservation and data protection in cyber‒physical systems;
  • Trust management and authentication in distributed CPS architectures;
  • Resilience and fault tolerance in cyber‒physical systems;
  • Secure software development and firmware design for CPS;
  • Hardware security for cyber-physical systems;
  • Machine learning and AI approaches for CPS security;
  • Case studies, real-world deployments, and lessons learned in securing cyber‒physical systems.

We encourage researchers to contribute their original work, case studies, and comprehensive reviews to enrich this Special Issue and provide valuable insights into securing the future of cyber‒physical systems.

We look forward to receiving your valuable contributions and making this Special Issue a comprehensive resource for researchers, practitioners, and policymakers interested in the security challenges and development prospects of emerging cyber‒physical systems.

Prof. Dr. Liviu C. Miclea
Prof. Dr. Răzvan Rughiniș
Dr. Ovidiu P. Stan
Guest Editors

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Keywords

  • cyber‒physical systems
  • security
  • threat modeling
  • risk assessment
  • secure communication
  • privacy preservation
  • intrusion detection
  • resilience
  • fault tolerance

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Published Papers (2 papers)

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Research

29 pages, 1297 KiB  
Article
Performance Modeling of Distributed Ledger-Based Authentication in Cyber–Physical Systems Using Colored Petri Nets
by Michał Jarosz, Konrad Wrona and Zbigniew Zieliński
Electronics 2025, 14(6), 1229; https://doi.org/10.3390/electronics14061229 - 20 Mar 2025
Viewed by 233
Abstract
Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an [...] Read more.
Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype. Full article
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12 pages, 1016 KiB  
Article
Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things
by Patibandla Pavithra Roy, Ventrapragada Teju, Srinivasa Rao Kandula, Kambhampati Venkata Sowmya, Anca Ioana Stan and Ovidiu Petru Stan
Electronics 2024, 13(3), 669; https://doi.org/10.3390/electronics13030669 - 5 Feb 2024
Cited by 6 | Viewed by 1730
Abstract
Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying [...] Read more.
Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN). Full article
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