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 June 2026 | Viewed by 2078

Special Issue Editors


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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

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

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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

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Published Papers (2 papers)

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Research

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
Viewed by 728
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)
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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
Viewed by 863
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)
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