Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.0
2.9
Journals
Electronics
Special Issues
New Challenges in IoT Security
electronics-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Electronics Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

New Challenges in IoT Security

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

Deadline for manuscript submissions: 15 February 2027 | Viewed by 3488

Editors

Dr. Mihai Doinea

E-Mail Website
Guest Editor
Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
Interests: data structures; mobile applications; IoT; biometrics; multimedia security; machine learning and Java card technology
Prof. Dr. Boja Catalin

E-Mail Website
Guest Editor
Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, Bucharest, Romania
Interests: computer science; cybersecurity; applied cryptography; IoT; software quality management; computer aided education

Special Issue Information

Dear Colleagues,

In recent years, IoT systems have become ubiquitous for our daily activities.

The Special Issue, New Challenges in IoT Security, seeks to collect contributions from researchers involved in designing and working with IoT systems, facing all sorts of security challenges. The Internet of Things (IoT) is rapidly transforming industries, societies, and daily life by connecting billions of devices across diverse environments. From smart cities and healthcare systems to industrial automation and critical infrastructure, IoT technologies are enabling unprecedented efficiency, intelligence, and connectivity. However, this explosive growth also presents critical security and privacy challenges. The increasing heterogeneity of devices, constrained computational resources, evolving attack surfaces, and large-scale data exchange have made IoT systems particularly vulnerable to cyber threats. These challenges demand innovative, multidisciplinary solutions that can ensure trust, resilience, and secure integration of IoT into the digital ecosystem.

This Special Issue on “New Challenges in IoT Security” seeks to bring together cutting-edge research and practical insights from the global community to address emerging issues in IoT protection. We invite original contributions that explore novel security models, architectures, algorithms, and frameworks aimed at safeguarding IoT environments. Topics of interest include, but are not limited to, lightweight cryptography for resource-constrained devices, intrusion detection and anomaly detection in IoT networks, secure communication protocols, privacy-preserving data management, AI- and blockchain-enabled security solutions, trust management frameworks, secure edge and cloud integration, and real-world case studies of IoT security deployments.

By uniting diverse perspectives, this Special Issue aims to foster dialogue between academia, industry, and policymakers. Contributions will not only advance theoretical understanding but will also highlight practical implementations and future directions for IoT security. The goal is to provide a comprehensive resource that will help researchers, practitioners, and decision-makers build resilient, trustworthy, and scalable IoT ecosystems capable of withstanding current and future cyber threats.

We warmly encourage the research community to submit their latest findings and join us in shaping the next generation of secure IoT technologies.

Dr. Mihai Doinea
Prof. Dr. Boja Catalin
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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
  • IoT security
  • privacy and trust management
  • lightweight cryptography
  • intrusion and anomaly detection
  • blockchain and AI-based security
  • secure edge and cloud integration

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (4 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

51 pages, 5687 KB  
Article
A Formal Model for Secure and Context-Based Data Dissemination in Federated Special IoT Environments
by Jakub Sychowiec and Zbigniew Zieliński
Electronics 2026, 15(15), 3407; https://doi.org/10.3390/electronics15153407 - 1 Aug 2026
Viewed by 179
Abstract
An increasing number of special Internet of Things (IoT) applications are being deployed within federated and zero-trust (ZT) environments. These ad-hoc networks consist of heterogeneous, resource-constrained devices from various administrative domains, all of which are susceptible to compromise. The dynamic nature of these [...] Read more.
An increasing number of special Internet of Things (IoT) applications are being deployed within federated and zero-trust (ZT) environments. These ad-hoc networks consist of heterogeneous, resource-constrained devices from various administrative domains, all of which are susceptible to compromise. The dynamic nature of these environments necessitates near-real-time Situational Awareness (SA), where processed data varies with its sensitivity and reliability, without dependence on a central authority. Examples include NATO and non-NATO coalitions engaged in hybrid military operations or humanitarian aid scenarios. To address the challenges of security, reliability, and context-aware data dissemination, we propose FedM, a multi-level formal model designed for context-aware and policy-driven data dissemination in federated IoT environments. This model is built upon various access control models and Denning’s research on information flow control (IFC), prioritizing the protection and reliability of data flows. A crucial element of this model is the distributed ledger, which facilitates the dynamic modification of label expressiveness, enhances resilience against disruption attacks, and separates policy logic from application functionality to mitigate risks associated with the benevolent developer. Additionally, we delineate a deterministic and history- and precedence-aware policy enforcement procedure to resolve conflicting actions and introduce processing primitives for the ongoing Data Quality Assessment (DQA) process. Our model also aligns with the concepts of Ubiquitous and Continuum Computing. Furthermore, in our paper we illustrate a policy-based dissemination pipeline, incorporating a bounded trustworthiness dimension. Additionally, we present a refined multi-layered framework that proposes the deployment of Information Flow Control (IFC) components, such as the Open Policy Agent decision engine, to facilitate policy-driven contextual data dissemination. We provide preliminary benchmarks for resource-constrained platforms, along with a formal threat model that addresses implicit flows, the benevolent developer problem, and the behavior of a distributed ledger under degraded network conditions. Finally, we conduct a formal verification of our model using the P framework. Full article
(This article belongs to the Special Issue New Challenges in IoT Security)
►▼ Show Figures

Figure 1

18 pages, 7706 KB  
Article
Software-Defined Runtime Reconfiguration of Cryptographic Service Chains for IIoT Edge Nodes
by Lei Zhang, Tianyu Luo, Huiyi Zhao, Chaoen Xiao and Jianxin Wang
Electronics 2026, 15(15), 3325; https://doi.org/10.3390/electronics15153325 - 28 Jul 2026
Viewed by 324
Abstract
In Industrial Internet of Things (IIoT) environments, the security requirements of edge nodes change dynamically, whereas conventional cryptographic deployment relies on static configurations that require firmware upgrades or system reboots for algorithm updates, severely limiting flexibility and maintainability. To address this issue, this [...] Read more.
In Industrial Internet of Things (IIoT) environments, the security requirements of edge nodes change dynamically, whereas conventional cryptographic deployment relies on static configurations that require firmware upgrades or system reboots for algorithm updates, severely limiting flexibility and maintainability. To address this issue, this paper proposes a software-defined runtime reconfiguration mechanism for cryptographic service chains on OS-capable IIoT edge nodes. By decoupling cryptographic processing logic definition from its execution environment, the control plane generates integrity-protected executable service chains, which are dynamically loaded and executed at runtime on the edge node. A prototype implemented on a Raspberry Pi edge node supports dynamic composition and switching among AES, SM4, SHA-256, and SM3, and incorporates HMAC-based integrity verification, version-based rollback prevention, and a smooth transition mechanism supported by kernel buffering. Experimental results show millisecond-level processing latency and a middleware overhead of 3.9–5.2% relative to a static baseline in the same Python interpreter environment, while the reconfiguration latency satisfies the real-time constraints of typical IIoT edge applications. The security and deployment boundaries of the proposed approach are also analyzed. Full article
(This article belongs to the Special Issue New Challenges in IoT Security)
►▼ Show Figures

Figure 1

16 pages, 2588 KB  
Article
Smart Home IoT Forensics in Matter Ecosystems: A Data Extraction Method Using Multi-Admin
by Sungbum Kim, Sungmoon Kwon and Taeshik Shon
Electronics 2026, 15(4), 884; https://doi.org/10.3390/electronics15040884 - 20 Feb 2026
Cited by 1 | Viewed by 1039
Abstract
As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data [...] Read more.
As the smart home ecosystem expands with the adoption of Matter, a wide variety of Internet of Things (IoT) devices are entering the market, and these devices are becoming more complex, as they support diverse functionalities. Consequently, smart home forensics often requires data extraction procedures that are specific to each device and platform, which increases the technical burden and time costs for investigators. To address these challenges, this study proposes a method that leverages Matter Multi-Admin support for multiple fabrics to enable efficient data acquisition from Matter-enabled IoT devices, regardless of the underlying smart home platform. This method configures a forensic Matter controller using chip-tool and commissions IoT devices that have already been commissioned to a smart home platform into a secondary fabric via Multi-Admin. The forensic controller then performs data extraction using standardized Matter interfaces. The proposed approach was validated on our smart home testbed by targeting a Matter smart bulb commissioned to the SmartThings platform and successfully extracting data generated by the platform, thereby demonstrating the utility of the method. The results indicate that the method enables nondestructive and efficient evidence acquisition from smart home IoT devices and can support future research and real-world investigations. Full article
(This article belongs to the Special Issue New Challenges in IoT Security)
►▼ Show Figures

Figure 1

30 pages, 2271 KB  
Article
Wavelet-Based IoT Device Fingerprinting
by Abdelfattah Amamra, Viet Nguyen, Adam Cheung, Sarah Acosta and Thuy Linh Pham
Electronics 2026, 15(4), 786; https://doi.org/10.3390/electronics15040786 - 12 Feb 2026
Cited by 1 | Viewed by 1185
Abstract
Accurate fingerprinting of Internet of Things (IoT) devices is essential for network security, management, and anomaly detection. Existing machine-learning-based approaches can be broadly classified into two categories. The first are time-domain-based approaches that infer device identity from aggregated traffic statistics, while effective in [...] Read more.
Accurate fingerprinting of Internet of Things (IoT) devices is essential for network security, management, and anomaly detection. Existing machine-learning-based approaches can be broadly classified into two categories. The first are time-domain-based approaches that infer device identity from aggregated traffic statistics, while effective in dense communication environments, they perform poorly for devices that generate sparse, low-volume, or irregular traffic, which restricts behavioral visibility. The second, radio frequency fingerprinting (RFF), extracts hardware-specific traits from radio frequency signals but is limited in wired or mixed-connectivity IoT networks and lacks behavioral or functional insights. To overcome these limitations, this paper proposes a hybrid fingerprinting framework that integrates network traffic analysis with frequency-domain representations using wavelet transform techniques. This approach captures both temporal and spectral characteristics, combining behavioral and structural perspectives to enable robust and accurate IoT device identification. The proposed system is evaluated on three real-world datasets under multiple experimental scenarios, including (1) device identification, (2) device type classification, (3) scalability with dataset size and complexity, and (4) performance under Distributed Denial-of-Service (DDoS) attack conditions. Experimental results show that wavelet-based features consistently outperform conventional time-domain features across all evaluation metrics, achieving higher accuracy, resilience, and generalization. Full article
(This article belongs to the Special Issue New Challenges in IoT Security)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop