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Electronics
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Research in Secure IoT-Edge-Cloud Computing Continuum
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Research in Secure IoT-Edge-Cloud Computing Continuum

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 7963

Special Issue Editors

Dr. Orhan Ermiş

E-Mail Website
Guest Editor
IT for Innovative Services, Luxembourg Institute of Science and Technology (LIST), 4362 Esch sur Alzette, Luxembourg
Interests: network security; applied cryptography; computer networks; privacy- enhancing technologies; machine learning
Dr. Carlos Serrão

E-Mail Website
Guest Editor
Department of Information Science and Technology, Iscte-Instituto Universitário de Lisboa, 1649-026 Lisboa, Portugal
Interests: cybersecurity; information security; distributed web and mobile-based information systems and applications; digital assets intellectual property protection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of the Internet of Things (IoT) and its associated technologies in recent decades has enhanced data collection, exchange, communication, and control regarding people, processes, and things. In tandem, edge computing and IoT converge have been employed to address the limitations of cloud computing in handling the deluge of real-time data. While the cloud provides unparalleled storage and computational capabilities, the sheer volume of data generated by IoT devices necessitates a more distributed and responsive approach. Edge computing acts as a strategic intermediary, enabling data processing closer to the point of generation. This collaborative relationship creates a seamless flow, where the edge handles immediate data needs, and the cloud, with its vast resources, takes on complex analytics, storage, and long-term processing; this forms a dynamic and interconnected ecosystem—the Computing Continuum. As organizations increasingly leverage these technologies to drive innovation and harness the power of data, ensuring the security and resilience of this continuum becomes essential. To tackle the challenges and enhance the benefits of various approaches, we require open, efficient, and collective threat intelligence tools that support the establishment of a culture for the security chain, covering communication, data collection, data transport, and data processing.

For this purpose, this Special Issue will be focusing upon, but not limited to the following topics:

  • AI-based solutions for the detection of DDoS attacks in IoT, Edge and/or Cloud;
  • Distributed and centralized measures for enabling privacy-aware computing;
  • Federated/Distributed Learning-based approaches for security;
  • Zero-trust architectures for the dynamic integration and management of IoT devices;
  • Collective threat intelligence and sharing;
  • Collaborative defense mechanisms;
  • (Self-sovereign) identity management solutions for IoT and Edge devices;
  • Secure and efficient data sharing solutions for the computing continuum;
  • Scalable (quantum-safe) key management solutions.

Dr. Orhan Ermiş
Dr. Carlos Serrão
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. Electronics 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 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

  • Internet of Things
  • cybersecurity
  • identity management
  • privacy
  • artificial intelligence
  • threat intelligence
  • collaborative defense
  • data sharing
  • key management
  • zero-trust

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Published Papers (4 papers)

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Research

21 pages, 5691 KiB  
Article
Task Offloading Strategy for UAV-Assisted Mobile Edge Computing with Covert Transmission
by Zhijuan Hu, Dongsheng Zhou, Chao Shen, Tingting Wang and Liqiang Liu
Electronics 2025, 14(3), 446; https://doi.org/10.3390/electronics14030446 - 23 Jan 2025
Viewed by 861
Abstract
Task offloading strategies for unmanned aerial vehicle (UAV) -assisted mobile edge computing (MEC) systems have emerged as a promising solution for computationally intensive applications. However, the broadcast and open nature of radio transmissions makes such systems vulnerable to eavesdropping threats. Therefore, developing strategies [...] Read more.
Task offloading strategies for unmanned aerial vehicle (UAV) -assisted mobile edge computing (MEC) systems have emerged as a promising solution for computationally intensive applications. However, the broadcast and open nature of radio transmissions makes such systems vulnerable to eavesdropping threats. Therefore, developing strategies that can perform task offloading in a secure communication environment is critical for both ensuring the security and optimizing the performance of MEC systems. In this paper, we first design an architecture that utilizes covert communication techniques to guarantee that a UAV-assisted MEC system can securely offload highly confidential tasks from the relevant user equipment (UE) and calculations. Then, utilizing the Markov Decision Process (MDP) as a framework and incorporating the Prioritized Experience Replay (PER) mechanism into the Deep Deterministic Policy Gradient (DDPG) algorithm, a PER-DDPG algorithm is proposed, aiming to minimize the maximum processing delay of the system and the correct detection rate of the warden by jointly optimizing resource allocation, the movement of the UAV base station (UAV-BS), and the transmit power of the jammer. Simulation results demonstrate the convergence and effectiveness of the proposed approach. Compared to baseline algorithms such as Deep Q-Network (DQN) and DDPG, the PER-DDPG algorithm achieves significant performance improvements, with an average reward increase of over 16% compared to DDPG and over 53% compared to DQN. Furthermore, PER-DDPG exhibits the fastest convergence speed among the three algorithms, highlighting its efficiency in optimizing task offloading and communication security. Full article
(This article belongs to the Special Issue Research in Secure IoT-Edge-Cloud Computing Continuum)
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22 pages, 981 KiB  
Article
Intelligent Platform for Automating Vulnerability Detection in Web Applications
by Diogo Moreira, João Pedro Seara, João Pedro Pavia and Carlos Serrão
Electronics 2025, 14(1), 79; https://doi.org/10.3390/electronics14010079 - 27 Dec 2024
Viewed by 1198
Abstract
In a world increasingly dependent on technology and in an era where connectivity is omnipresent, Web applications have become an essential part of our everyday life. The evolution of these applications, combined with the exponential increase in the number of users, has brought [...] Read more.
In a world increasingly dependent on technology and in an era where connectivity is omnipresent, Web applications have become an essential part of our everyday life. The evolution of these applications, combined with the exponential increase in the number of users, has brought with it not only convenience but also significant challenges in terms of security. Ensuring the security of Web applications and their data is increasingly a priority for companies, although many companies lack the know-how, time, and money to do so. This research project studied and developed a system with the aim of automating the process of detecting vulnerabilities in Web applications by exploiting the benefits of the interoperability of the two forms of automation of the tool selected to carry out this analysis. The developed solution is low-cost and requires very little user intervention. In order to validate and evaluate the developed platform, experiments were carried out on applications with different types of vulnerabilities known in advance and on real applications. It is essential to guarantee the security of Web applications, and the developed system proved capable of automating the detection of vulnerability risks and returning the results in a relatively simple way for the user. Full article
(This article belongs to the Special Issue Research in Secure IoT-Edge-Cloud Computing Continuum)
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24 pages, 1016 KiB  
Article
ProtectingSmall and Medium Enterprises: A Specialized Cybersecurity Risk Assessment Framework and Tool
by Mohammed El-Hajj and Zuhayr Aamir Mirza
Electronics 2024, 13(19), 3910; https://doi.org/10.3390/electronics13193910 - 2 Oct 2024
Viewed by 3156
Abstract
As the number of Small and Medium Enterprises (SMEs) rises in the world, the amount of sensitive data used also increases, making them targets for cyberattacks. SMEs face a host of issues such as a lack of resources and poor cybersecurity talent, resulting [...] Read more.
As the number of Small and Medium Enterprises (SMEs) rises in the world, the amount of sensitive data used also increases, making them targets for cyberattacks. SMEs face a host of issues such as a lack of resources and poor cybersecurity talent, resulting in multiple vulnerabilities that increase overall risk. Cybersecurity risk assessment frameworks have been developed by multiple organizations such as the National Institute of Science and Technology (NIST) and the International Organization for Standardization (ISO), but they are complicated to understand and challenging to implement. This research aimed to create an effective cybersecurity risk assessment framework specifically for SMEs while considering their limitations. This was achieved by first identifying common threats and vulnerabilities and categorizing them according to their importance and risk. Secondly, popular frameworks like the NIST CSF and ISO 27001/2 were analyzed for their proficiencies and deficiencies while identifying relevant areas for SMEs. Finally, novel techniques catered to SMEs were explored and incorporated to create an effective framework for SMEs. This framework was also developed in the form of a tool, providing an interactive and dynamic environment. The tool was effective, and the framework is a promising start but requires more quantitative analysis. Full article
(This article belongs to the Special Issue Research in Secure IoT-Edge-Cloud Computing Continuum)
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34 pages, 765 KiB  
Article
Securing Federated Learning: Approaches, Mechanisms and Opportunities
by Mohammad Moshawrab, Mehdi Adda, Abdenour Bouzouane, Hussein Ibrahim and Ali Raad
Electronics 2024, 13(18), 3675; https://doi.org/10.3390/electronics13183675 - 16 Sep 2024
Cited by 3 | Viewed by 2269
Abstract
With the ability to analyze data, artificial intelligence technology and its offshoots have made difficult tasks easier. The tools of these technologies are now used in almost every aspect of life. For example, Machine Learning (ML), an offshoot of artificial intelligence, has become [...] Read more.
With the ability to analyze data, artificial intelligence technology and its offshoots have made difficult tasks easier. The tools of these technologies are now used in almost every aspect of life. For example, Machine Learning (ML), an offshoot of artificial intelligence, has become the focus of interest for researchers in industry, education, healthcare and other disciplines and has proven to be as efficient as, and in some cases better than, experts in answering various problems. However, the obstacles to ML’s progress are still being explored, and Federated Learning (FL) has been presented as a solution to the problems of privacy and confidentiality. In the FL approach, users do not disclose their data throughout the learning process, which improves privacy and security. In this article, we look at the security and privacy concepts of FL and the threats and attacks it faces. We also address the security measures used in FL aggregation procedures. In addition, we examine and discuss the use of homomorphic encryption to protect FL data exchange, as well as other security strategies. Finally, we discuss security and privacy concepts in FL and what additional improvements could be made in this context to increase the efficiency of FL algorithms. Full article
(This article belongs to the Special Issue Research in Secure IoT-Edge-Cloud Computing Continuum)
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